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In this study, we examined the effect of mindfulness meditation facilitated by human-robot interaction (HRI) on brain activity. EEG signals were collected from two groups of participants; Meditation group who practiced mindfulness meditation with a social robot and Control group who only listened to a lecture by the robot. We compared brain functional connectivity between the two groups by computing EEG phase synchrony during HRI session. The results revealed a significantly lower global phase synchrony in beta frequency band of the Meditation group, which has been previously reported as an indication of reduced cognitive processing and achieving the mindful state in experienced meditators. Our findings demonstrate the potential of Socially-Assistive Robots (SAR) for integration in mental healthcare and optimization of the intervention effects. Additionally, our study puts forward new measures for objective monitoring of HRI effect on the user's neurophysiological responses. [ABSTRACT FROM AUTHOR]

Copyright of CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings is the property of Conferencia Iberica de Sistemas Tecnologia de Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this research is to develop a new noninvasive method of a non-verbal communication system for the patients those who are struggling with a total locked-in syndrome, such as Amyotrophic Lateral Sclerosis (ALS) patient in the final stage and etc., using Brain-Computer Interface (BCI). In this research, the experiment was divided into two parts, 8x8 and 32x32 matrix checkerboard pattern stimuli reversed at frequencies of 3 and 6Hz. The most remarkable result from this data was that the significant differences between not attending to the stimulus and attending to each stimulus were observed with the p-value below 0.005 in the case of 8x8 matrix checkerboard pattern stimulus. Based on the results, the feasibility of using the checkerboard pattern as a stimulus in BCI, when it is used as a visual stimulus for BCI, was also discussed in this paper. [ABSTRACT FROM AUTHOR]

This research introduces the adaptive behavior training game platform using behavior and reading intervention mechanisms combined with narrative contents. For this, "The Fisherman and the Jinni" from The Book of the Thousand Nights and a Night, also known as The Arabian Nights, is refined and used as its narrative story telling source. The behavior and reading intervention scheme makes the narrative contents drive a player like a child with ADHD to help a cyber character, the Fisherman. While the child is playing the game, the platform continuously captures attention and meditation values of the player through a brain-computer interface (BCI), and the motions of the player using a motion sensing device. In addition to the sensing data, all the intervention history between the player and the game platform is recorded in a cloud engine for real-time analysis and post evaluation of the treatments. The contribution of this research includes the effort to applying emerging technology into behavioral therapy for children with ADHD to improve their behavior and reading skills. The result of the experiments with a total of three first children not diagnosed with ADHD shows the effectiveness of this approach in terms of stimulating the children to complete the given behavior and reading tasks and to feel a sense of accomplishment. Pre- and post-test with general reading comprehension questions and survey to ask their willingness to save the Jinni indicate changes on their decision making procedures influenced by their emotional assimilation into the given narrative story. [ABSTRACT FROM AUTHOR]

The Brain-Computer Interface (BCI) is the only available technology for patients in advanced ALS when eye-tracking based devices could be no longer used because of loss of gaze control. The BCI can convert brain electrical signals into commands able to control external devices without need of any voluntary movement. Among different approaches of signal analysis, a recent BCI system, the BrainControl Basic Communicator (BC), based on event related desynchronization (ERD) produced by motor imagery (MI), has been recently developed by Liquidweb s.r.l. and proposed in the current project. The aim of this study was to verify the efficacy of the Braincontrol BC as an AAC device and to validate the training methodology with regards of ALS patients in locked-in state. The study was conducted, from 2012 to 2015, on two groups: one of 34 ALS patients and another of 63 healthy users. The results of this observation confirm that the device, after the first phase training, is efficient and robust for patients in advanced ALS state. Trainings have been completed successfully for all the healthy users and ALS patients in initial and severe stage of the disease; only 2 of 34 patients located in the ALS patients group failed the training. In particular, these 2 patient were in completely locked-in state, in which cognitive status information where unknown. After this study, 17 locked-in patients, that really needs this technology, have continued to use the system as a communicator. [ABSTRACT FROM AUTHOR]

Motion Onset Visually Evoked Potentials (mVEPs) have the advantage of being an elegant and less visual fatiguing stimuli than that of other VEPs such as the steady state VEP (SSVEP) or P300 and therefore may be apposite for use in movement-free brain-controlled computer games using brain-computer interface (BCI) technology. To investigate the effects of 3-Dimensional (3D) graphic variations on mVEPs, we present in this pilot study a set of five hypothetical game levels with differing graphics, each with increasing visual complexity, in which the user/player must attend to one of five mVEP inducing stimuli. The mVEP based on-screen virtual buttons involves a leftward motion lasting 140 milliseconds to elicit a response from the dorsal pathway. This pilot study focused on offline classification results. BCI classification accuracy results for separating target vs. non target mVEP stimuli (2 class) as well as classifying target stimuli among the five stimuli (5 class) are compared for each variation in graphic complexity. The results of the study show a trend indicating the classification accuracy is inversely proportional to graphic complexity however the difference in BCI classification accuracy for each level of complexity are not significant (>70%, p>0.05). The results are encouraging, suggesting that the use of 3D graphics of varied complexity is possible when using mVEP based BCI as a control strategy. [ABSTRACT FROM AUTHOR]

We present a concept and a pilot study of a two-step input speller application combined with a spatial auditory brain-computer interface (BCI) for locked-in syndrome (LIS) users. The application has been developed for 25 Japanese syllabary (hiragana) characters using a two-step input procedure, in order to create an easy-to-use BCI-speller interface. In the proposed procedure, the user first selects the representative letter of a subset, defining the second step. In the second step, the final choice is made. At each interfacing step, the user's intentional choices are classified based on the P300 event related potential (ERP) responses captured in the EEG, as in the classic oddball paradigm. The BCI experiment and EEG results of the pilot study confirm the effectiveness of the proposed spelling method. [ABSTRACT FROM AUTHOR]

This paper addresses an important problem known as EEG non-stationarity in Brain-computer Interfacing. We propose a novel technique called Dynamically Weighted Classification with Clustering (DWCC), which explores hidden states in non-stationary EEG using a modified k-means clustering method by combining cosine distance measure and mutual information criterion. DWCC builds a set of classifiers, one for each pair of clusters from different classes. A dynamically-weighted classifier ensemble network is trained to combine the outputs of the classifiers, where we propose to dynamically assign the weight of a classifier for each test sample based on its distances to the cluster centres associated with the classifier. Experimental results on publicly available BCI Competition IV Dataset 2a yielded a mean accuracy of 81.5% which is statistically significant (t-test p<0.05) compared to the baseline result of 75.9% using a single classifier. [ABSTRACT FROM PUBLISHER]

Brain-computer interface (BCI) is a system that captures and decodes electroencephalogram (EEG) signals and transforms human thoughts into actions. To achieve this goal, using classification algorithms are most popular approach. However, classification of EEG signals can be categorized in complex problems because of high nonlinearity, high dimensionality, poor signal to noise ratio and poor spatial resolution. Combining classifiers is an approach to improve the performance of complex problems. In this article we studied the application of combining classifiers based on wavelet features to improve the performance of EEG signal classification in BCI systems. Three normal subjects K3b, K6b and L1b were asked to perform imaginary movements of left hand, right hand, tongue and foot during predefined time interval. EEG signals were decomposed into wavelet coefficients by discrete wavelet transform and used as feature vectors, presenting them into classifiers. Four combining classifiers were used to evaluate the EEG signals. Experimental results show that wavelet transform is an appropriate tool for the analyzing EEG signals. Also, according to the results of the experiments, mixture of experts overcomes the other used combining methods. [ABSTRACT FROM PUBLISHER]

This paper presents our development of an integrated brain-driven micro-biomanipulation system that can perform mind-controlled biomanipulation at micro scale. The system incorporates a non-invasive electroencephalogram (EEG) device with a high-precision automated micromanipulator through high speed network. The human manipulation mind measured by the EEG device can effectively drive the micro-manipulator to perform the 2-D manipulation on bio-samples. During the manipulation, the trace of human manipulation mind is monitored by a custom-built high-precision position sensing detector (PSD) interface unit. Extensive experimental results demonstrate the high performance of the developed network-enabled and brain-driven micro-biomanipulation system. The research work can be further used to investigate manipulation behavior and neurobiofeedback mechanism of human brain for facilitating development of high-efficiency micro-biomanipulation strategy of engineering approaches in micro/nano level. [ABSTRACT FROM PUBLISHER]

Electroencephalogram (EEG) record brain activation into electric millivolts. Brain computer interface based on EEG signal recognized brain control limbs movement is one of the main application of BCI system. Classification attention of user when moving limbs find out the brain region activating and translate them into commands which could control outer device. Recently, a famous technique to analyze EEG signals based on spatial filter is common spatial patterns (CSP). It is popular for two-class paradigm when maximize one class in the same time minimize the other one. In the other hand, CSP gets limitation when just working on covariance matrices in which not only stationary brain signal sources but also contaminated non-stationary sources. In this paper, we proposed to applied extension of CSP to separate non-stationary and also apply for multi-class BCI to reduce these disadvantage mentioned above. To solve non-stationary sources problem, we applied stationary CSP (sCSP) to separate signal sources. sCSP method is also powerful for binary paradigm. To improve sCSP for multi-class, we applied joint approximate diagonalization (JAD), which is successful to find efficient spatial filter in the context of multi-class BCI. Since CSP supposed to separate data space linearly, nearest neighbor method was used to classify the multi-class BCI to evaluate the performance of methods. We used 2 sorts of dataset: 1) auditory cue to two patients respond to three kinds of sound for executive movement; 2) auditory spatial from 2 speakers separate sounds. Three kinds of sound are: 500Hz left hand, 2000Hz right hand and noise sound ignored. [ABSTRACT FROM PUBLISHER]

To increase information transfer rate (ITR) in brain-computer interface (BCI), many approaches better extracting new and robust features, or effectively combining such features have been proposed. Recently, hybrid BCI combining features simultaneously acquired from two different experimental paradigms has been reported. In this work, subjects performed tasks typically used in two BCI approaches, namely event-related de-synchronization (ERD) and steady-state somatosensory evoked potentials (SSSEP). Subjects imagined the movement of left or right hand (ERD) and gave more attention to the vibration (having different frequencies) of left or right finger (SSSEP). Offline analyses were found that half of the subjects did well ERD BCI rather than SSSEP BCI and information that was invisible in ERD pattern appeared in SSSEP pattern. As a preliminary study to develop a hybrid BCI using both ERD and SSSEP, we investigated how different information could be extracted from ERD and SSSEP, and further discussed how the hybrid ERD-SSSEP BCI is feasible. [ABSTRACT FROM PUBLISHER]

Experiments were performed to determine if discrete feedback could be discriminated during concurrent performance of motor imagery for brain-computer interface control. An inverse solution feature extraction method captured regions of activity indicative of conflict detection and nterpretation of feedback. Separate classifiers were trained to detect positive and negative feedback based on constrained rates of precision. Tested in simulation, classification rates suggest discrimination of discrete feedback embedded within a trial can improve accuracy in less able brain-computer interface subjects, with little impact on subjects who perform well. [ABSTRACT FROM PUBLISHER]

Recently, a lot of researchers have studied the brain-computer interface (BCI) system, and some systems have already been put to practical use. For instance, the system based on P300 were actively used. However the P300 system requires user's strong concentrations. Therefore it is necessary to introduce information from the EEG signal. This paper tried to investigate a rhythmic after-discharge of transient-VEP by consecutive visual stimulation, and now have examined responses to a variety of stimulation methods. [ABSTRACT FROM PUBLISHER]

Brain-Computer Interface (BCI) system is an advanced and emerging system in the field of computer applications. Every day road accidents are at an increase due to drunken driving and sleepiness. This paper proposes an EEG based BCI system to sense the Oscitancy which can be due to drunkenness and sleepiness. The proposed work consists of two modules, the sensor and microprocessor module and an alarm device module. The advantages of the system are low power consumption and small volume. So, it can be applicable in real-time driving conditions. [ABSTRACT FROM PUBLISHER]

Brain-computer interfaces (BCI) provide direct and non-muscular communication methods for the people with severe motor impairments. Event-related potentials (ERPs) as efficient modals are commonly used in some of the BCI systems, including visual stimulus, auditory stimulus as well as tactile stimulus. In this experiment, the corresponding Chinese pronunciations were inserted into the visual Oddball series of 1–9 numbers to carry out the cross-sense stimuli of BCI. The experimental data analysis result proves that the P300 components produced by visual-auditory associate stimulation have higher amplitudes and shorter latencies than those produced by visual-only stimulus. For further analysis the constrained independent component analysis (cICA) method was applied when extracting the signal features of ERP and the support vector machine (SVM) method was used to BCI classification. Result proves that the ERPs produced by visual-auditory associate stimulation modal have better recognition efficiency than those in visual-only stimulation. It can relevant the capacity of information alteration in BCI and is worth to do more studies. [ABSTRACT FROM PUBLISHER]

Providing adaptive shared control for Brain-Computer Interfaces (BCIs) can result in better performance while reducing the user's mental workload. In this respect, online estimation of accuracy and speed of command delivery are important factors. This study aims at real-time differentiation between fast and slow trials in a motor imagery BCI. In our experiments, we refer to trials shorter than the median of trial lengths as “fast” trials and to those longer than the median as “slow” trials. We propose a classifier for real-time distinction between fast and slow trials based on estimates of the entropy rates for the first 2–3 s of the electroencephalogram (EEG). Results suggest that it can be predicted whether a trial is slow or fast well before a cutoff time. This is important for adaptive shared control especially because 55% to 75% of trials (for the five subjects in this study) are longer than that cutoff time. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel method of designing a class-discriminative spatial filter assuming that a combination of spatial pattern vectors, irrespective of the eigenvalues, can produce better performance in terms of classification accuracy. We select discriminative spatial pattern vectors that determine features in a pairwise manner, i.e., eigenvectors of the k-th largest eigenvalue and the k-the lowest eigenvalue. Although the pair of the eigenvectors of the K largest and the K smallest eigenvalues helps extract discriminative features, we believe that a different set of eigenvector pairs is more appropriate to extract class-discriminative features. In our experiments, the proposed method outperformed the conventional approach. [ABSTRACT FROM PUBLISHER]

In this paper, the preliminary investigation for application of capacitive electroencephalogram (EEG) measurement for brain-computer interface (BCI) is described. EEG was obtained by active electrodes that very high input impedance pre-amplifier circuit was mounted on the electrodes. This method enabled EEG measurements through hair without direct contact with scalp and conductive gel applications. The presented electrode was attached to the normal usual baseball cap for nonintrusive daily or long-term use. Subject wore electrode installed baseball cap and EEG signal was measured under experimental BCI paradigms such as alpha wave, steady-state visual evoked potential, P300, auditory steady-state response and motor imagery. The results showed that further studies are still required to apply capacitive sensing methods to clinical neurosciences, however, it can be concluded that steady of the art capacitive electrode technology can be used for engineering applications using current popular BCI paradigms. [ABSTRACT FROM PUBLISHER]

In this study, an neural-network-based system is proposed for the applications of brain-computer interface (BCI). Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system consists of three procedures, including enhanced active segment selection, feature extraction, and classification. Firstly, combined with the use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the active-segment selection. Multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. Finally, support vector machine (SVM) is used for classification. Compared with other approaches on motor imagery data, the results indicate that the proposed method is promising in BCI applications. [ABSTRACT FROM PUBLISHER]

Classification methods have been widely applied in most brain computer interfaces (BCIs) that control devices for better quality of life. Most existing classification methods for P300-based BCIs extract features based on temporal structure related to P300 components of event-related potentials (ERPs). Some others exploit the spatial distribution of ERPs optimally selected by recursive channel elimination. However, none of them employed multilinear structures which exploit hidden features in P300-based BCI data. In this paper, we propose a new feature extraction method based on tensor decomposition for ERP-based BCIs. The method seeks an optimal feature subspace simultaneously spanned by temporal and spatial bases, and additional bases which indicate a variant of ERPs obtained by different degrees of polynomial fittings. The proposed method has been evaluated by both the BCI competition III data set II and the affective face driven paradigm data set, and achieved 92% and 95% classification accuracies respectively, which were better than those of most existing P300-based BCI algorithms. [ABSTRACT FROM PUBLISHER]

The traditional brain-computer interface (BCI) system measures the electroencephalography (EEG) signals by the wet sensors with the conductive gel and skin preparation processes. To overcome the limitations of traditional BCI system with conventional wet sensors, a wireless and wearable multi-channel EEG-based BCI system is proposed in this study, including the wireless EEG data acquisition device, dry spring-loaded sensors, a size-adjustable soft cap. The dry spring-loaded sensors are made of metal conductors, which can measure the EEG signals without skin preparation and conductive gel. In addition, the proposed system provides a size-adjustable soft cap that can be used to fit user's head properly. Indeed, the results are shown that the proposed system can properly and effectively measure the EEG signals with the developed cap and sensors, even under movement. In words, the developed wireless and wearable BCI system is able to be used in cognitive neuroscience applications. [ABSTRACT FROM PUBLISHER]

Recognizing Event-Related Desynchronization or Synchronization (ERD/ERS) patterns generated by motor imagery tasks is an important process in Brain-computer interfaces (BCI). One of the most well-known algorithms to extract the discriminative patterns is Common Spatial Patterns (CSP). It finds an optimal spatial filter considering the spatial distribution of the ERD/ERS patterns. The CSP algorithm, however, does not consider temporal information of the Electroencephalogram (EEG) signals even though EEG signals are naturally non-stationary. In order to circumvent the limitation, in this paper, we propose a novel method, Time-Dependent Common Spatial Patterns (TDCSP) to classify multi-class motor imagery tasks. We optimize CSP filters in multiple local time ranges of EEG signals individually based on statistical analysis to effectively reflect changes of discriminative spatial distributions over time. We evaluated the proposed method by experiments on BCI Competition IV dataset 2-a, which resulted in high performance outperforming the previous methods in the literature. [ABSTRACT FROM PUBLISHER]

A wireless portable electroencephalogram (EEG) recording system for animals is designed, manufactured and then tested in a rat. The system basically consists of four modules: 1) EEG collecting module with the wireless transmitter and receiver (designed by NRF24LE1), 2) filter bank consisting of preamplifier, band pass filter and 50 Hz trapper, 3) power management module and 4) display interface for showing EEG signals. The EEG data are modulated firstly and emitted by the wireless transmitter after being amplified and filtered. The receiver demodulates and displays the signals in voltage through serial port. The system is designed as surface mount devices (SMD) with small size (20 mm × 25 mm × 3 mm) and light weight (4 g), and is fabricated of electronic components that are commercially available. To test the system, measuring electrodes were implanted on the rat's skull and the acquisition transmitter was strapped on the back of the rat. Waveform recordings were carried out during waking state. The test results indicate that in given environment the system could stably record more than 8 hours and transmit EEG signals over a distance of 20 m. Our system shows the features of small size, low power consumption and high accuracy which is suitable for EEG telemetry in rats. [ABSTRACT FROM PUBLISHER]

In this study, we implemented a new mental spelling system based on steady-state visual evoked potential (SSVEP), adopting a QWERTY style layout keyboard with 30 LEDs flickering with different frequencies. During the offline experiments performed with five participants, we optimized various factors influencing the performance of the mental spelling system, such as distances between adjacent keys, light source arrangements, stimulating frequencies, recording electrodes, and visual angles. The online experiments were conducted with six participants to verify the feasibility of the optimized mental spelling system. The results of the online experiments were an average typing speed of 9.39 letters per minute (LPM) with an average success rate of 87.58 % corresponding to an average information transfer rate of 40.72 bits per minute, demonstrating the high performance of the developed mental spelling system. Indeed, the average typing speed of 9.39 LPM attained in this study was one of the best LPM results among those reported in previous BCI literatures. To further enhance the performance of our mental spelling system, we combined eye direction information (‘left’ or ‘right’) extracted from a web camera with the SSVEP responses. As a result of the online experiments performed with 10 participants, the hybrid speller could reduce 16.6 typing errors on average. [ABSTRACT FROM PUBLISHER]

In this study, various feature extraction methods for motor-imagery-based BCI were evaluated in terms of robustness to slight changes in electrode locations. EEG signals were recorded from three reference electrodes (Fz, C3, and C4) and from six additional electrodes located close to the reference electrodes. The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated in terms of robustness to electrode location changes as well as regarding absolute classification accuracy. The quantitative evaluation results demonstrated that the use of either PSD- or CC-based features led to higher classification accuracy than the use of PLV-based features, whereas PSD-based features showed much higher sensitivity to changes in EEG electrode location than CC- or PLV-based features. There results suggest that CC can be a promising feature extraction method in motor-imagery-based BCI studies as it provides high classification accuracy along with being little affected by slight changes in the EEG electrode locations. [ABSTRACT FROM PUBLISHER]

Neuronal power attenuation or enhancement in specific frequency bands over the sensorimotor cortex, called Event-Related Desynchronization (ERD) or Event-Related Synchronization (ERS), respectively, is a major phenomenon in brain activities involved in imaginary movement of body parts. However, it is known that the nature of motor imagery-related electroencephalogram (EEG) signals is non-stationary and highly variable over time and frequency. In this paper, we propose a novel method of finding a discriminative time- and frequency-dependent spatial filter, which we call ‘non-homogeneous filter.’ We adaptively select bases of spatial filters over time and frequency. By taking both temporal and spectral features of EEGs in finding a spatial filter into account it is beneficial to be able to consider non-stationarity of EEG signals. In order to consider changes of ERD/ERS patterns over the time-frequency domain, we devise a spectrally and temporally weighted classification method via statistical analysis. Our experimental results on the BCI Competition IV dataset II-a clearly presented the effectiveness of the proposed method outperforming other competing methods in the literature. [ABSTRACT FROM PUBLISHER]

A novel method based on Time-Frequency correction was presented to extract motor imagery feature from brain signal. The 2b datasets of BCI competition IV were used in this paper. Time-frequency features of right and left hand motor imagery were extracted from train datasets, respectively. The frequency band and time segment were selected based on frequency and time correction. The features were extracted from selected frequency band and time segment. The results showed that the classification accuracy using the features from selected frequency band and time segment is higher than that using the normal band power features. [ABSTRACT FROM PUBLISHER]

In the present study, a new paradigm for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) was proposed, which can be used for disabled individuals with impaired oculomotor function. The proposed EEG-based BCI system allows users to express their binary intentions without a need to open their eyes. A pair of glasses with two LEDs flickering at different frequencies was used to present visual stimuli to participants with their eyes closed. Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. After customizing the parameters of the proposed paradigm based on the offline analysis results, binary intentions of five participants were classified in real time. The average ITR of our online experiments reached 10.83 bits/min, demonstrating the feasibility of our BCI paradigm. [ABSTRACT FROM PUBLISHER]

Brain-computer interface (BCI) systems are not often used as input devices for modern games, due largely to their low bandwidth. However, BCIs can become a useful input modality when adapting the dynamics of the brain-game interaction, as well as combining them with devices based on other physiological signal to make BCIs more powerful and flexible. We introduce the Graz BCI Game Controller (GBGC) and describe how techniques such as context dependence, dwell timers and other intelligent software tools were implemented in a new system to control the Massive Multiplayer Online Role Playing Game World of Warcraft (WoW). [ABSTRACT FROM PUBLISHER]

This proposal is about a brain-controlled electrically powered wheelchair. The system comprises a brain-computer interface based on steady-state visual evoked potentials and a processing unit relying on a finite state machine (FSM). Results of first simulation experiments comparing two different FSMs are presented. [ABSTRACT FROM AUTHOR]