Search

Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation. This research involved twenty-two healthy, right-handed participants. EEG data were collected using 21 electrodes positioned over the motor cortex, while two electromyography (EMG) electrodes recorded the onset of ankle joint movements. The study focused on analyzing slow cortical potential (SCP) and sensorimotor rhythms (SMR) in alpha and beta bands from the EEG. Five key features—fourth-order Autoregressive feature, variance, waveform length, standard deviation, and permutation entropy—were extracted. A modified Recurrent Neural Network (RNN) including Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms was developed for movement recognition. These were compared against conventional machine learning algorithms, including nonlinear Support Vector Machine (SVM) and k Nearest Neighbourhood (kNN) classifiers. The performance of the proposed models was assessed using two data schemes: within-subject and across-subjects. The findings demonstrated that the GRU and LSTM models significantly outperformed traditional machine learning algorithms in recognizing different EEG signal quantities for intralimb movement. The study indicates that deep learning models, particularly GRU and LSTM, hold superior potential over standard machine learning techniques in identifying intralimb movements using EEG signals. Where the accuracies of LSTM for within and across subjects were 98.87 ± 1.80 % and 87.38 ± 0.86 % respectively. Whereas the accuracy of GRU within and across subjects were 99.18 ± 1.28 % and 86.44 ± 0.69 % respectively. This advancement could significantly benefit the development of BCI devices aimed at foot rehabilitation, suggesting a new avenue for enhancing physical therapy outcomes.

This systematic review provides a comprehensive analysis of studies that use electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to investigate how acute stress affects decision-making processes. The primary goal of this systematic review was to examine the influence of acute stress on decision making in challenging or stressful situations. Furthermore, we aimed to identify the specific brain regions affected by acute stress and explore the feature extraction and classification methods employed to enhance the detection of decision making under pressure. Five academic databases were carefully searched and 27 papers that satisfied the inclusion criteria were found. Overall, the results indicate the potential utility of EEG and fNIRS as techniques for identifying acute stress during decision-making and for gaining knowledge about the brain mechanisms underlying stress reactions. However, the varied methods employed in these studies and the small sample sizes highlight the need for additional studies to develop more standardized approaches for acute stress effects in decision-making tasks. The implications of the findings for the development of stress induction and technology in the decision-making process are also explained.

Worker safety and productivity are crucial for effective job management. Interruptions to an individual’s work environment and their impact on mental health can have adverse effects. One prospective instrument for assessing and calculating an individual’s mental state in an interrupted scenario and cognitive demand levels is the use of physiological computing devices in conjunction with behavioral and subjective measurements. This study sought to address how to gather and compute data on individuals’ cognitive states in interrupted work settings through critical analysis. Thirty-three papers were considered after the literature search and selection procedure. This descriptive study is conducted from three perspectives: parameter measurement, research design, and data analysis. The variables evaluated were working memory, stress, emotional state, performance, and resumption lag. The subject recruitment, experimental task design, and measurement techniques were examined from the standpoint of the experimental design. Data analysis included computing and cognitive pre-processing. Four future research directions are suggested to address the shortcomings of the present studies. This study offers suggestions for researchers on experiment planning and using computing to analyze individuals’ cognitive states during interrupted work scenarios. Additionally, it offers helpful recommendations for organizing and conducting future research.

This research seeks to examine the impact of workstation types on the coupling of neural and vascular activities of the prefrontal cortex (PFC). The design of the workstations was found to impair the performance, physical and mental health of employees. However, the mechanism underlying cognitive activity involved during workstation design-related stress effects in the PFC has not been fully understood. We used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to simultaneously measure electrical activity and hemoglobin concentration changes in the PFC. The multimodal signal was collected from 23 healthy adult volunteers who completed the Montreal imaging stress task in ergonomic and non-ergonomic workstations. A supervised machine learning method based on temporally embedded canonical correlation analysis (tCCA) was utilized to obtain the association between neural activity and local changes in hemoglobin concentrations to enhance localization and accuracy. The results showed deactivation in alpha power rhythms and oxygenated hemoglobin, as well as declined activation pattern of the fused data in the right PFC at the non-ergonomic workstation. Additionally, all participants at the non-ergonomic workstation experienced a substantial rise in salivary alpha-amylase activity in comparison with the ergonomic workstation, indicating the existence of high-stress levels. The proposed tCCA approach obtains excellent results in discriminating workstation types achieving accuracies of 98.8% and a significant improvement of 8.0% (p

Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Although many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from CT scans is critical for identifying liver cancer. In this article, deeply dense-network ResU-Net architecture is implemented on CT scan using the 3D-IRCADb01 dataset. An essential feature of ResU-Net is the residual block and U-Net architecture, which extract additional information from the input data compared to the traditional U-Net network. Before being fed to the deep neural network, image pre-processing techniques are applied, including data augmentation, Hounsfield windowing unit, and histogram equalization. The ResU-Net network performance is evaluated using the dice similarity coefficient (DSC) metric. The ResU-Net system with residual connections outperformed state-of-the-art approaches for liver tumour identification, with a DSC value of 0.97% for organ recognition and 0.83% for segmentation methods.

Sleepiness has been recognized as a causal factor in many round-the-clock industries. While individuals can subjectively express their momentary sleepiness level, sleepiness-related contextual factors (CF) can influence their perception of sleepiness and cognitive performance. In this paper, the self-reported sleepiness value (vSRS) was improved by transforming it into a kernel density estimate and the assignment of the class's score is done using a likelihood ratio test (IvSRS). We integrated multiple CF and IvSRS to model sleepiness using a Bayesian network (BN). The BN produced a single probability estimate calculated based on the prior and posterior probability of the CF and IvSRS. The results showed IvSRS performed better (p 1-measure > 78%.

In this paper, we investigated the relationships between EEG and semantic long-term memory retrieval using spectral analysis and functional connectivity. We employed an EEG data set from a previous study, which was recorded from 34 healthy participants during a memory retrieval process. The data set consists of EEG collected during two tasks, a learning task, in which participants studied animated learning material, and a memory recall task, in which they recalled the learned information. We analyzed both EEG signals and behavioral responses. We conducted spectral analyses for the absolute power and relative power of EEG frequency bands, EEG coherence, absolute phase delay, and EEG amplitude asymmetry features. The delta-band EEG and theta-band absolute power were negatively correlated with task performance at frontal regions, while the alpha-band activity was positively correlated in frontal regions and negatively correlated in parietal and temporal sites. High-beta in the frontal lobe was positively correlated with memory recall score. EEG coherence was negatively correlated in the delta band at the fronto-parietal and fronto-central sites, and positively correlated in the gamma band at widespread regions, including all lobes. EEG phase delay showed the highest degree of connectivity in the delta and alpha bands at frontal sites, and was negatively correlated with memory recall score. The negative correlation in phase delay demonstrated the synchronization and positive correlation reflected the de-synchronization of electrode pairs during memory recollection in the delta and alpha frequencies. We concluded that neuronal networks producing low EEG frequencies at frontal regions and those producing high frequencies in widespread regions are active during the memory recollection process. Thus, semantic memory storage involves widespread neuronal networks across the brain.

This study aims to investigate the effects of workplace noise on neural activity and alpha asymmetries of the prefrontal cortex (PFC) during mental stress conditions. Workplace noise exposure is a pervasive environmental pollutant and is negatively linked to cognitive effects and selective attention. Generally, the stress theory is assumed to underlie the impact of noise on health. Evidence for the impacts of workplace noise on mental stress is lacking. Fifteen healthy volunteer subjects performed the Montreal imaging stress task in quiet and noisy workplaces while their brain activity was recorded using electroencephalography. The salivary alpha-amylase (sAA) was measured before and immediately after each tested workplace to evaluate the stress level. The results showed a decrease in alpha rhythms, or an increase in cortical activity, of the PFC for all participants at the noisy workplace. Further analysis of alpha asymmetry revealed a greater significant relative right frontal activation of the noisy workplace group at electrode pairs F4-F3 but not F8-F7. Furthermore, a significant increase in sAA activity was observed in all participants at the noisy workplace, demonstrating the presence of stress. The findings provide critical information on the effects of workplace noise-related stress that might be neglected during mental stress evaluations.

Driving distraction is a topic of great interest in the transport safety-research community, because it is now a primary cause of road accidents. A recent report has revealed that distraction is more alarming than previously thought, and a suitable measurement to effectively detect distraction is required. Most agree that driving distraction actually comprises the simultaneous interaction of two or more types of distraction. The purpose of this paper is, therefore, to determine the promising method for measuring visual cognitive distraction. We discuss the five common measurement methods for visual and cognitive driving distraction, which include driving performance, driver physical measures, driver biological measures, subjective reports, and hybrid measures. Hybrid measurement of driver's physical measures (e.g., eye movement) and driver's biological measures (e.g., electroencephalogram) is better than other methods at detecting types of visual cognitive distraction. This new perspective on measurement methods will help the field of transport safety to determine the best means of detecting and measuring the effect of visual cognitive distraction.