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This paper presents Multi-view Leap2 Hand Pose Dataset (ML2HP Dataset), a new dataset for hand pose recognition, captured using a multi-view recording setup with two Leap Motion Controller 2 devices. This dataset encompasses a diverse range of hand poses, recorded from different angles to ensure comprehensive coverage. The dataset includes real images with the associated precise and automatic hand properties, such as landmark coordinates, velocities, orientations, and finger widths. This dataset has been meticulously designed and curated to maintain a balance in terms of subjects, hand poses, and the usage of right or left hand, ensuring fairness and parity. The content includes 714,000 instances from 21 subjects of 17 different hand poses (including real images and 247 associated hand properties). The multi-view setup is necessary to mitigate hand occlusion phenomena, ensuring continuous tracking and pose estimation required in real human-computer interaction applications. This dataset contributes to advancing the field of multimodal hand pose recognition by providing a valuable resource for developing advanced artificial intelligence human computer interfaces. [ABSTRACT FROM AUTHOR]

This paper presents an interpretable, spiking neural classifier (IpT-SNC) with time-varying weights. IpT-SNC uses a two-layered spiking neural network (SNN) architecture in which weights of synapses are modeled using amplitude-modulated, time-varying Gaussian functions. Self-regulated particle swarm optimization (SRPSO) is used to update the amplitude, width, and centers of the Gaussian functions and thresholds of neurons in the output layer. IpT-SNC has been developed to improve the interpretability of spiking neural networks. The time-varying weights in IpT-SNC allow us to describe the rationale behind predictions in terms of specific input spikes. The performance of IpT-SNC is evaluated on ten benchmark datasets in the UCI machine learning repository and compared with the performance of other learning algorithms. According to the performance results, IpT-SNC enhances classification performance on testing datasets from a minimum of 0.5% to a maximum of 7.7%. The significance level of IpT-SNC with other learning algorithms is evaluated using statistical tests like the Friedman test and the paired t-test. Furthermore, on the challenging real-world BCI (Brain Computer Interface) competition IV dataset, IpT-SNC outperforms current classifiers by about 8% in terms of classification accuracy. The results indicate that IpT-SNC has better generalization performance than other algorithms. [ABSTRACT FROM AUTHOR]

The human brain functions through electrical signals. By measuring these signals, one can monitor brain activity and gain insights into the brain function of the subject. An electroencephalogram (EEG) allows one to monitor brain activity by having the subject wear an array of sensors on their head. This process is frequently used to diagnose medical conditions such as epilepsy. In recent years, there have been efforts to use EEG signals in concert with deep learning to create a brain computer interface (BCI). Such a device would enable the wearer to communicate to a system via brain signals. While such a system would not be so advanced as to enable the translation of complex thoughts, it would enable a user to command a machine to perform a small number of functions. The objective of this paper was to develop and optimize recurrent neural network architectures for use with a brain computer interface. Using EEG data collected from subjects, a variety of neural network models were created to learn from the data. The models that were used were simple recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU). This paper proposes a novel approach to EEG signal classification, demonstrating the capabilities of recurrent networks which are seldom explored for this purpose. This study produced promising results for recurrent models, obtaining a 91% accuracy with the 4-layer LSTM architecture. This presents a solid foundation for the argument that LSTM and similar architectures are feasible for BCI applications. [ABSTRACT FROM AUTHOR]

Introduction: Brain computer interfaces (BCI), which establish a direct interaction between the brain and the external device bypassing peripheral nerves, is one of the hot research areas. How to effectively convert brain intentions into instructions for controlling external devices in real-time remains a key issue that needs to be addressed in brain computer interfaces. The Riemannian geometry-based methods have achieved competitive results in decoding EEG signals. However, current Riemannian classifiers tend to overlook changes in data distribution, resulting in degenerated classification performance in cross-session and/or cross subject scenarios. Methods: This paper proposes a brain signal decoding method based on Riemannian transfer learning, fully considering the drift of the data distribution. Two Riemannian transfer learning methods based log-Euclidean metric are developed, such that historical data (source domain) can be used to aid the training of the Riemannian decoder for the current task, or data from other subjects can be used to boost the training of the decoder for the target subject. Results: The proposed methods were verified on BCI competition III, IIIa, and IV 2a datasets. Compared with the baseline that without transfer learning, the proposed algorithm demonstrates superior classification performance. In contrast to the Riemann transfer learning method based on the affine invariant Riemannian metric, the proposed method obtained comparable classification performance, but is much more computationally efficient. Discussion: With the help of proposed transfer learningmethod, the Riemannian classifier obtained competitive performance to existingmethods in the literature. More importantly, the transfer learning process is unsupervised and time-efficient, possessing potential for online learning scenarios. [ABSTRACT FROM AUTHOR]

In the field of neuroscience, a significant challenge lies in extracting essential features from biological signals like Electroencephalography (EEG). Utilized as a non-invasive method, EEG records brain activities through metal electrodes on the scalp. The analysis of EEG data finds applications in various domains, including concealed information tests, aimed at detecting deception. This paper introduces the Spatial Spiking Neural Network, a supervised approach for classifying EEG data collected during concealed information tests. Temporal EEG data undergoes filtration using a Finite Impulse Response (FIR) filter, while Common Spatial Pattern (CSP) is employed to extract spatial components. Binary classification is achieved through an integrate-and-fire neuron model, where the frequency of spike generation determines the classification. Spiking Neural Networks (SNNs) offers advantages in terms of temporal precision, event-driven processing, and low power consumption. Their spike-based communication allows for efficient handling of sparse data and recognition of temporal patterns, contributing to robustness and energy efficiency. The proposed model is applied separately to each subject's EEG data, and the results are compared with traditional classification algorithms. The proposed approach attains a peak accuracy of 90.15%, showcasing superior performance compared to alternative methods. [ABSTRACT FROM AUTHOR]

A brain-computer interface (BCI) based on an electroencephalograph (EEG) establishes a new channel of communication between the human brain and a computer. Redundant, noisy, and irrelevant channels lead to high computational costs and poor classification accuracy. Therefore, an effective feature selection technique for determining the optimal number of channels can improve BCI's performance. However, existing meta-heuristic algorithms are prone to get trapped in local optimum due to high dimensional dataset. Thus, to reduce dimension, solve inter subject variation and choose an optimal subset of channels, a novel framework called Component Loading followed by Clustering and Classification (CLCC) is proposed in this paper. This novel framework is further divided into two experiment configurations-CLCC with Feature Selection (CLCC-FS) and CLCC without Feature Selection (CLCC-WFS). All these frameworks have been implemented on a motor imagery (MI) EEG dataset of 10 subjects in order to choose the best subset of channels. Further, seven different classifiers have been employed to assess the performance. Experimental outcomes show that on comparing various feature selection techniques, our proposed algorithm i.e., CLCC-FS Opposition-Based Whale Optimization Algorithm (CLCC-FS(OBWOA)) performed substantially better than the other feature selection techniques. We demonstrate that the proposed algorithm is able to achieve 99.6% accuracy by using only few channels and can improve the practicality of the BCI system by reducing the computation cost. [ABSTRACT FROM AUTHOR]

Automatically describing the content of a picture with meaningful and contextually appropriate textual descriptions is a difficult challenge at the interface of computer vision and natural language processing. This review paper provides a thorough summary of recent developments in picture caption generating methods, including both conventional strategies and cutting-edge deep learning-based solutions. We examine the changes in datasets, evaluation criteria, and model designs that have influenced this field's advancement. In order to improve the standard and variety of generated captions, we also integrate visual attention processes, transformer-based models, and reinforcement learning techniques. The focus of this study is on the algorithmic overlap between text to speech and visual. [ABSTRACT FROM AUTHOR]

Introduction: Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color. Methods: This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects. Results: By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual. Discussion: The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP). [ABSTRACT FROM AUTHOR]

In recent years the popularity of smartphones has been on the rise, the importance of user experience is being discussed more frequently, and interface design is determined as a crucial technique in the interactive design of mobile devices. As both supply and demand for smartphones have grown, the interactive interface designs are significant, and attention is paid to their effects. The traditional method is only applicable to computers, has a limited user base, and has poor efficiency and effectiveness. However, the operation and functionality of mobile phones are diminished due to the installation of unwanted applications. Hence it is particularly evident in the interactive art approach in which there has been limited innovation in core capabilities and few distinguishing features among more established programmes. Inorder to perform speedy progress, the Convolutional Neural Network (CNN) and Deep Learning (DL) are widely determined. Furthermore, Style Transfer and Target Detection are emerging as popular because of their significance to the field of visual research. So this paper proposes the prevalent quasi- and flat-materialized style and analyzes the skills necessary for applications. The proposed method achieved superior performance when validated with MPI Sintel and Train2020 MSCOCO datasets. [ABSTRACT FROM AUTHOR]

Over the last 50 years education has been transformed by digital technologies. Many efforts have been made to create and apply "digital" teaching and learning methods, tools and platforms. The last 25 years of computer-based education can be characterized by the availability of digital information sources and the implementation and operation of digital learning management platforms based on the Internet. The question about further meaningful and effective progress in the next decades is openly discussed. Many expect strong influences and changes from artificial intelligence systems that generate contextualized information from various sources. Some see interactive virtual worlds expanding and partially replacing the physical world. Many believe in the further development of learning management and communication platforms. Others do not expect much valuable change at all due to the slow pace of complex educational systems with recent studies even showing a decline in the quality of educational outcomes during the last five years as a result of even too much digitalization in education. This paper discusses these positions with an emphasis on the roles of humans and computers and their interfaces, i.e. Human-Computer Interaction, for future learning and teaching with rapidly changing information technologies in the next 25 and 50 years. [ABSTRACT FROM AUTHOR]

Though a computer professional, Meredith Broussard feels "that the way people talk about technology is out of touch with what digital technology can actually do", and that seeing the latter as a panacea has "resulted in a tremendous amount of poorly designed technology" (6), which needlessly complicates instead of improving or making life easier. Artificial Unintelligence: How Computers Misunderstand the World: by Meredith Broussard, Cambridge, MA, MIT Press, 2019, 248 pp., $15.95T/£12.99 (paper) Supported by extensive research, I Artificial Unintelligence i cogently challenges the prevailing technophile hype extolling the unlimited ways in which technology supposedly can "change the world for the better" and create a digital utopia with infinite benefits in every area of life. [Extracted from the article]

A new type of interface using a conduction hot spot reflecting the user's intention is presented. Conventional methods using fingertips to generate conduction hot points cannot be applied to those who have difficulty using their hands or cold hands. In order to overcome this problem, an exhaling interaction using a hollow rod is proposed and extensively analyzed in this paper. A preliminary study on exhaling interaction demonstrated the possibility of the method. This paper is an attempt to develop and extend the concept and provide the necessary information for properly implementing the interaction method. We have repeatedly performed conduction hot-point-generation experiments on various materials that can replace walls or screens to make wide use of the proposed interfaces. Furthermore, a lot of experiments have been conducted in different seasons, considering that the surface temperature of objects also changes depending on the season. Based on the results of an extensive amount of experiments, we provide key observations on important factors such as material, season, and user condition, which should be considered for realizing contactless exhaling interfaces. [ABSTRACT FROM AUTHOR]

The ZX Spectrum was a popular 8-bit home computer by Sinclair Research in the 1980s. Even though some of these computers may still work, the audio tapes, the TV with an analog tuner, and the micro-switch joystick that were used with the original ZX Spectrum are outdated and hard to find in good working order or to replicate. As many other old closed systems are also very difficult to update to support modern peripherals there is a necessity to provide a methodology to adapt such systems to support new peripherals while being compatible with existing software. This implementation is a means by which to validate the methodology before applying it to a physical system. The work proposed in this paper focused on recreating a ZX Spectrum+/48K computer and interfacing it with modern peripherals on an FPGA. This was accomplished by adding a co-processor to assist with the control of the more complex peripherals. Otherwise, the original system would require complex architectural changes and would perform poorly due to the low performance of the Z80 CPU. This work distanced itself from previous works on emulating a ZX Spectrum, as it focused on the use of different upgraded peripherals and the use of a NIOS II soft processor as a co-processor to manage the SD card accesses and save-state functionality. A demonstration of the proposed modernized architecture was made by successfully running a diagnostics ROM and playing original ZX Spectrum games from an SD card for games with a PS/2 keyboard and a pair of joysticks. [ABSTRACT FROM AUTHOR]

Tongue-drive system (TDS) is an advanced assistive technology (AT) that enables people with severe disabilities to independently control the electronic devices and machines using their tongue. Through the use of a small magnetic tracer, it can be translated into user-defined commands. Real time system accuracy is the major parameter of effectiveness of such AT's. So, a projected model needs a hardware based real time implementations or optimum model development with reconfigurable architectures before design of application specific ICs. This paper reviews various tongue drive systems and their features and approaches to the brain, tongue, and computer interface. In this paper, the data rate of serial peripheral interfaces compared and discussed the attenuation techniques and their signal-to-noise ratio for processing the data. The projected machine learning classifiers mainly decision tree, k-Nearest Neighbor, logistic regression, Naïve Bayes, random forest, and support vector machine are compared in terms of accuracy, F1 score, and power consumption. The performance comparison of TDS and implementation methods on field programmable logic arrays are discussed. The design and simulation work done so far on MATLAB and Xilinx environment are mainly analyzed to identify future scope of work. Also, the fortes and drawbacks of these systems and approaches are included. [ABSTRACT FROM AUTHOR]

Optical orthogonal frequency-division multiplexing (OFDM) has proven to be an effective technique for the visible light communication (VLC) system in which data is sent and received through the intensity-modulated/direct-detected optical channel. However, OFDM exhibits high peak-to-average power ratio (PAPR) and for the generation of real-valued signal employs Hermitian symmetry (HS) of the frequency symbols which in turn boost the computational complexity (CC) and deteriorates the performance of the system. Besides, OFDM also included cyclic prefix (CP) as redundancy that reduces the spectral efficiency. This paper aims to target the aforesaid OFDM issues and introduce a novel technique in the form of wavelet transform (WT) based on non-HS OFDM (NHS-OFDM). In the proposed technique, an N-point complex OFDM signal is generated by utilizing wavelets as basis function, from which real and imaginary components are extracted and then concatenated to form a 2N-point real signal. The work also confirmed that the CC of the proposed technique is about 83% less than the conventional Fourier transform (FT) based NHS-OFDM technique due to which reduction in PAPR and bit error rate (BER) improvement is also noticed from the simulated results. The superior performance of WT based NHS-OFDM technique is further verified via experiments conducted by interfacing the computer terminal with Raspberry Pi (RPi) 3b+ modules. In the designed VLC prototype, the NHS-OFDM modem work as the software-defined radio (SDR) on a stand-alone computer. The experiment results reveal that real-time text message is transmitted and received without error at the communication range of 1.75 m while the data rate attains from the prototype is 3.921 Mbps. [ABSTRACT FROM AUTHOR]

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The onset of the COVID-19 pandemic has led to an increased demand for mental health interventions, with a special focus on digitally assisted ones. Self-regulation describes a set of meta-skills that enable one to take control over his/her mental health and it is recognized as a vital indicator of well-being. Mindfulness training is a promising training strategy for promoting self-regulation, behavioral change, and mental well-being. A growing body of research outlines that smart technologies are ready to revolutionize the way mental health training programs take place. Artificial intelligence (AI); extended reality (XR) including virtual reality (VR), augmented reality (AR), and mixed reality (MR); as well as the advancements in brain computer interfaces (BCIs) are ready to transform these mental health training programs. Mindfulness-based interventions assisted by smart technologies for mental, emotional, and behavioral regulation seem to be a crucial yet under-investigated issue. The current systematic review paper aims to explore whether and how smart technologies can assist mindfulness training for the development of self-regulation skills among people at risk of mental health issues as well as populations with various clinical characteristics. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions using a total of sixty-six experimental studies that met the inclusion criteria. The results showed that digitally assisted mindfulness interventions supported by smart technologies, including AI-based applications, chatbots, virtual coaches, immersive technologies, and brain-sensing headbands, can effectively assist trainees in developing a wide range of cognitive, emotional, and behavioral self-regulation skills, leading to a greater satisfaction of their psychological needs, and thus mental wellness. These results may provide positive feedback for developing smarter and more inclusive training environments, with a special focus on people with special training needs or disabilities. [ABSTRACT FROM AUTHOR]

BACKGROUND: The stability criterion approach is very important for estimating precise behavior before or after fabricating brain computer interface system applications. OBJECTIVE: A novel approach using the Routh-Hurwitz standard criterion method is proposed to easily determine and analyze the stability of brain computer interface system applications. Using this developed approach, we were able to easily test the stability of technical issue using simple programmed codes before or after brain computer interfaces fabrication applications. METHODS: Using a MATLAB simulation program package, we are able to provide two different special case examples such as a first zero element and a row of zeros to verify the capability of our proposed Routh-Hurwitz method. RESULTS: The MATLAB simulation program provided efficient Routh-Hurwitz standard criterion results by differentiating the highest coefficients of the s and a. CONCLUSION: This technical paper explains how to use our proposed new Routh-Hurwitz standard condition to simply ascertain and determine the brain computer interface system stability without customized commercial simulation tools. [ABSTRACT FROM AUTHOR]

This paper devised a novel Christmas‐tree structure for flexible neural probes, which features recording sites on side‐branches that can be folded along the shank of the probe by temporary encapsulation before implantation. This design reduces the size of the implant and minimizes the surgical footprint. Upon implantation, the temporary encapsulation dissolves in vivo, leading to self‐stretching of the branches, and an expansion of the lateral detecting range of the probe. We conducted in vitro experiment with our probe in 0.6% agar that mimics the brain tissue environment. The results demonstrate that the neural probe we have developed exhibits self‐stretching ability and provides an increased lateral recording range without causing additional damage to the brain tissue. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This review provides a comprehensive examination of recent developments in both neurofeedback and brain-computer interface (BCI) within the medical field and rehabilitation. By analyzing and comparing results obtained with various tools and techniques, we aim to offer a systematic understanding of BCI applications concerning different modalities of neurofeedback and input data utilized. Our primary objective is to address the existing gap in the area of meta-reviews, which provides a more comprehensive outlook on the field, allowing for the assessment of the current landscape and developments within the scope of BCI. Our main methodologies include meta-analysis, search queries employing relevant keywords, and a network-based approach. We are dedicated to delivering an unbiased evaluation of BCI studies, elucidating the primary vectors of research development in this field. Our review encompasses a diverse range of applications, incorporating the use of brain-computer interfaces for rehabilitation and the treatment of various diagnoses, including those related to affective spectrum disorders. By encompassing a wide variety of use cases, we aim to offer a more comprehensive perspective on the utilization of neurofeedback treatments across different contexts. The structured and organized presentation of information, complemented by accompanying visualizations and diagrams, renders this review a valuable resource for scientists and researchers engaged in the domains of biofeedback and brain-computer interfaces. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) provides a wealth of physiological and psychological information. Decoding EEG signals enables machines to recognize brain activity, a crucial aspect in brain-computer interaction and medical rehabilitation. However, the non-stationarity and inter-individual variability of EEG signals pose challenges for existing EEG signal classification models to achieve the desired level of cross-subject generalization, limiting the practical applications of EEG-based brain computer interface systems. Unsupervised Domain Adaptation (UDA) aims to improve the model's generalization performance on the target domain by minimizing the discrepancies between the source and target domain data distributions. Many researchers treat different subjects as distinct domains and utilize Unsupervised Domain Adaptation (UDA) in transfer learning to guide the model for effective cross-subject EEG Classification. Strategies involve reducing distribution discrepancies between the source and target domains by minimizing well-designed discrepancy metrics or using an adversarial discriminator to capture domain-invariant features. However, neglecting category-discriminatory (abbreviated as discriminative) features leads to the limited effectiveness of these domain-level alignment methods. To tackle these issues, we propose the Discriminative Clustering Domain Adaptation Network (DCDAN), aimed at utilizing an unsupervised approach to construct discriminative information for facilitating Unsupervised Domain Adaptation (UDA) in achieving category-level feature alignment between the source and target domains. Specifically, we employ a clustering algorithm based on adversarial domain adaptation to associate pseudo-labels with target domain samples. Implementing a self-supervised training framework enables the model to acquire discriminative features. Moreover, we introduce the Pseudo Label-Common Spatial Pattern (PL-CSP) component, which integrates the prediction confidence of pseudo-labels from target domain samples with discriminative information from source domain samples through a covariance matrix weighting strategy based on sample confidence. This integration enhances the robustness of the learned spatial filter on the target domain. Experimental results on public datasets demonstrate the effectiveness of our proposed method in extracting more discriminative features. [ABSTRACT FROM AUTHOR]

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems. [ABSTRACT FROM AUTHOR]

Gesture recognition is a mechanism by which a system recognizes an expressive and purposeful action made by a user's body. Hand-gesture recognition (HGR) is a staple piece of gesture-recognition literature and has been keenly researched over the past 40 years. Over this time, HGR solutions have varied in medium, method, and application. Modern developments in the areas of machine perception have seen the rise of single-camera, skeletal model, hand-gesture identification algorithms, such as media pipe hands (MPH). This paper evaluates the applicability of these modern HGR algorithms within the context of alternative control. Specifically, this is achieved through the development of an HGR-based alternative-control system capable of controlling of a quad-rotor drone. The technical importance of this paper stems from the results produced during the novel and clinically sound evaluation of MPH, alongside the investigatory framework used to develop the final HGR algorithm. The evaluation of MPH highlighted the Z-axis instability of its modelling system which reduced the landmark accuracy of its output from 86.7% to 41.5%. The selection of an appropriate classifier complimented the computationally lightweight nature of MPH whilst compensating for its instability, achieving a classification accuracy of 96.25% for eight single-hand static gestures. The success of the developed HGR algorithm ensured that the proposed alternative-control system could facilitate intuitive, computationally inexpensive, and repeatable drone control without requiring specialised equipment. [ABSTRACT FROM AUTHOR]

As new methods for interacting with systems are being developed for use within augmented or virtual reality, their impact on the quality of the user's experience needs to be assessed. Although many instruments exist for evaluating the overall user experience or the computer interface used to complete tasks, few provide measures that can be used to evaluate the specific forms of interaction typically used in these environments. This paper describes the development of a customizable questionnaire for measuring the subjective user experience that focuses on the quality of the interactions with objects in augmented reality/virtual reality (AR/VR) worlds, which we are calling the Customizable Interactions Questionnaire, or (CIQ). The final questionnaire measures five factors that are related to user satisfaction while using the system: quality of interactions, assessment of task performance, comfort, quality of sensory enhancements, and consistency with expectations. [ABSTRACT FROM AUTHOR]

Emotion is an important factor that decides the the state of the mind of an individual. However, there are many people who cannot express their emotions explicitly due to various psychological or physiological issues. The recent technology development in interdisciplinary techniques has made the emotion recognition easy. The main objective of this paper is to uncover the contribution of hybrid deep learning model in classifying the emotions. This novel hybrid Bi-LSTM model is applied to three dimensional VAD model to classify 16 emotions. In addition, this paper addresses their performance comparison with respect to other models. Deep learning models like CNN, LSTM, Hybrid Bi-LSTM, and Hybrid Bi-GRU were used for experimentation. FFT is used to convert from time domain to frequency domain in all the models. The performance measure of these models is estimated in terms of accuracy, precision, recall, and F1score. The estimated accuracy of different deep learning models are approximated to be 87.5% for CNN, 88.7 % for LSTM, 93.9% for Hybrid Bi-LSTM, and 92.2% for Hybrid GRU based on the number of subjects. These comparisons have helped to find the suitable deep learning model for emotion recognition. [ABSTRACT FROM AUTHOR]

Brain Computer Interface is an emerging technology for assisting patients having long term disability. Electroencephalography is the best technique for recording neural activities, however, recording bio signal data from patients is a hectic task. Therefore, Data augmentation using generative adversarial networks is expected to solve this problem. In this paper, we present temporal generative adversarial network with conditional input labels to learn the spatio-temporal features of electroencephalograph and generate realistic data. In order to evaluate the performance of proposed model, we introduce qualitative, quantitative and feature visualization techniques on motor imagery electroencephalograph dataset. Further, we used 1-dimensional temporal convolutional neural network to investigate whether augmented data can replace real data or not for training models. The presented data augmentation framework was compared to three more data augmentation techniques like: copula generative adversarial network, conditional tabular generative adversarial network and tabular variational autoencoder. Our presented technique showed significantly better results to other data augmentation techniques for Electroencephalograph data. The experimental results show that the value of Kullback–Leibler Divergence (KL Divergence) and Inverted Kolmogorov Smirnov D static test (KS test) of data generated by proposed model is the lowest i.e., 0.8940 and 0.7674 respectively. Further, we have tested exhaustively our new data and used a new testing strategy achieving an accuracy of 94.1%. Finally, it is concluded that a time series-based data augmentation technique for Electroencephalograph can reduce long calibration process and thus improve the overall performance of motor imagery user action classification. [ABSTRACT FROM AUTHOR]

The field of brain–computer interface (BCI) enables us to establish a pathway between the human brain and computers, with applications in the medical and nonmedical field. Brain computer interfaces can have a significant impact on the way humans interact with machines. In recent years, the surge in computational power has enabled deep learning algorithms to act as a robust avenue for leveraging BCIs. This paper provides an up-to-date review of deep and hybrid deep learning techniques utilized in the field of BCI through motor imagery. It delves into the adoption of deep learning techniques, including convolutional neural networks (CNNs), autoencoders (AEs), and recurrent structures such as long short-term memory (LSTM) networks. Moreover, hybrid approaches, such as combining CNNs with LSTMs or AEs and other techniques, are reviewed for their potential to enhance classification performance. Finally, we address challenges within motor imagery BCIs and highlight further research directions in this emerging field. [ABSTRACT FROM AUTHOR]

Objective: In recent years, motor imagery-based brain--computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement. Approach: Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movementrelated cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm. Main results: As a result, both the MRCP and ERD features showed the specific temporal--spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively. Significance: This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs. [ABSTRACT FROM AUTHOR]

Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system. [ABSTRACT FROM AUTHOR]

Introduction: Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insu cient movement execution features in EEG signals. Methods: This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement. Results: A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%. Discussion: This approach can be used to develop a hybrid EEG-EMG brain- computer interface to provide patients with more accurate neural feedback to aid their recovery. [ABSTRACT FROM AUTHOR]

Based on the brain signals, decoding and analyzing the gait features to make a reliable prediction of action intention are the core issues in the brain computer interface (BCI)-based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting, and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of each brain region. Through the statistical analysis of these parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis, and logistic regression. Experimental results show the proposed method can better realize the recognition of the aforementioned action intention. The recognition accuracy of standing, sitting, and quiet of 13 subjects is higher than 80.9%, and the highest one can reach 86.8%. Directed dynamic brain network analysis of the 8 brain regions shows that the occurrence of lower limb movement will weaken the dependence between brain regions, resulting in the weakening of network topological connection. The result has significant value for understanding human's brain cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation. [ABSTRACT FROM AUTHOR]

According to recent legislative initiatives, non-consensual pornography is a crime in several countries and social media providers have a duty to provide their users easy to use mechanisms to report abuses. In this paper, we analyse the state of the art of the interfaces for reporting non-consensual pornography from the victim's perspective. Firstly, we analysed 45 content sharing platforms where aggressors might post non-consensual pornography. The analysis identified three distinct interaction styles for reporting the crime: Scriptum (a text-field where the user verbally describes the abuse), Bonam (a multilayered menu that includes a correct option), and Malam (a multilayered menu that does not include a correct option). Secondly, we conducted a within-subject study to evaluate the experience elicited by these interaction styles. Participants (N = 39) were given a scenario and asked to report six blurred images as non-consensual pornography using a medium-fidelity prototype. The results exposed complex trade-offs between clarity, efficiency, and distress among the different interaction styles. These trade-offs open foundational research directions transcending boundaries between human-computer interaction and multimedia studies and interfacing computer science research with the law. [ABSTRACT FROM AUTHOR]

A systematic review is a type of literature review that aims to collect and analyse all available evidence from the literature on a particular topic. The process of screening and identifying eligible articles from the vast amounts of literature is a time‐consuming task. Specialised software has been developed to aid in the screening process and save significant time and labour. However, the most suitable software tools that are available often come with a cost or only offer either a limited or a trial version for free. In this paper, we report the release of a new software application, Catchii, which contains all the important features of a systematic review screening application while being completely free. It supports a user at different stages of screening, from detecting duplicates to creating the final flowchart for a publication. Catchii is designed to provide a good user experience and streamline the screening process through its clean and user‐friendly interface on both computers and mobile devices. All in all, Catchii is a valuable addition to the current selection of systematic review screening applications. It enables researchers without financial resources to access features found in the best paid tools, while also diminishing costs for those who have previously relied on paid applications. Catchii is available at https://catchii.org. [ABSTRACT FROM AUTHOR]

The wear detection of the guide pair (GP) plays a key role in the safe operation of the mine hoist system. Due to the actual working conditions of the well, manual detection is still the main detection method for GP wear, which has the problems of time consumption, low detection accuracy, and being unable to realize real-time detection. In view of this situation, this paper studies a machine vision-based wear detection method of GP in a mine hoisting system. First, the wear detection algorithm of GP is designed by means of image correction, image preprocessing, and edge extraction. Then, the hardware of the detection system is selected and designed, and the interface of the upper computer is designed by LABVIEW. Finally, according to the actual underground working conditions, a test platform for the wear detection system is built, and the detection experiment is carried out. The results show that the method can detect the wear and the location of the GP's wear in real time. The maximum average error of the detection under three different wear conditions is 3.54%, which meets the requirements of the specified measurement accuracy. It can provide technical support for the automatic detection of the wear of GP in mine hoisting systems. [ABSTRACT FROM AUTHOR]

One of the issues in manufacturing is implementing the standard for the exchange of product data-numerical control (STEP-NC) data interface model on computer numeric control (CNC) machines. The most often used STEP-NC programming techniques for this implementation are indirect, interpreted, and adaptive. Because of the ease of integration with existing control systems, the performance of the interpreted method was noticeably superior to that of the indirect and adaptive approaches. This concept has resulted in the creation of several tools, systems, designs, algorithms, and methods. In this study, a new STEP-NC implementation system has been created, in which the interpretation has been done using entity-plus string-based (double layer) for more precise data extractions, the tool paths system can create facing, pocket, drill, bore, ream, countersink, side, slot, and contour operations, the output file generation system can create output as per interpreted and hybrid programming approaches, and the execution system can handle multi-threaded operations. To enhance the overall interpretation system and automate implementation by reducing manual intervention, an expert system has also been incorporated. The STEP-NC part 21 examples 1 and 2 part programs were manufactured on the CNC prototype to validate the technology. The creation of the system, the design of the algorithm, the experimental verification, the conclusion, and the future suggestions are described in the paper's content. [ABSTRACT FROM AUTHOR]

Brain computer interface (BCI) has many useful applications to help disabled people that have an active brain with difficulties in movements and speaking. One of these applications is the wheelchair, this device is operated always by just one user no sharing or borrowing the device. One-user applications need features extraction methods with high classification accuracy and small training datasets, the variability of the subjects’ mood during the recorded sessions and the tiredness during the long sessions are serious problems that affect the classification accuracy in these applications. Transfer learning can solve the problem, by recording short and separated sessions for the same subject in different training times or days. The proposed method in this paper uses motor imagery (MI) signals from different recorded sessions by one user to build an acceptable size training dataset. To regularize different recording sessions, four tuning parameters that are independent from each other are generated using a loop, these parameters are used to find the ratios of the covariance matrices. The suggested method gives very good performance using a different number of training samples compared with six different common spatial patterns (CSP) methods using only two channels. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCI) are a channel that implements direct communication between the brain and some external unit. Developments of BCIs can provide new application opportunities in a large number of fields of use. In the development of BCI devices, the development of technology and digital technology represented a big change, as it provided the necessary computing power to implement and run the continuously developing signal processing algorithms that ensure processing and evaluation. The aim of this paper is to provide an overview of BCI research results which were published in the engineering field. In the present study, articles that had a greater impact, where the annual average number of citations is greater than 30, in the BCI field were reviewed and processed in a systematic way, in order to make individual research more comparable. The systematic processing was focused on the aims of application, used device/dataset, applied data process and achieved best accuracy. This systematic study summarizes the most effective methods used in the BCI processing and highlights the future trends. The results showed an accuracy of 85% thanks to increasingly reliable, accurate and cost-effective signal detection and processing devices, as well as algorithms. [ABSTRACT FROM AUTHOR]

Continuous progress in disruptive technologies such as artificial intelligence (AI) and control theory has prompted considerable improvements in BCI applications. Brain-Machine Interface, also called Brain-Computer Interface (BCI), is an emerging research field with diverse applications ranging from rehabilitation to human augmentation. Practically speaking, control algorithms for critical components of the BCI algorithm have a high level of important and are often called a decoder. [Extracted from the article]

Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions. Steady state visually evoked potentials enable EEG vision-based brain computer interfaces. We show that it is possible to encode information distributed across 100+ frequencies. We encode images and perform simple physical computing classification tasks. Connecting more than one in brain in series improves the classification capability. [ABSTRACT FROM AUTHOR]

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