Predicting the Health Impacts of Commuting Using EEG Signal Based on Intelligent Approach

Commuting to work is an everyday activity for many which can have a significant effect on our health. Commuting on regular basis can be a cause of chronic stress which is linked to poor mental health, high blood pressure, heart rate, and exhaustion. This research investigates the neurophysiological and psychological impact of commuting in real-time, by analyzing brain waves and applying machine learning. The participants were healthy volunteers with mean age of 30 years. Portable electroencephalogram (EEG) data were acquired as a measure of stress level. EEG data were acquired from each participant using non-invasive NeuroSky MindWave headset for 5 continuous activities during their commute to work. This approach allowed effects to be measured during and following the period of commuting. The results indicate that whether the duration of commute was low or large, when participants were in a calm or relaxed state the bio-signal alpha band exceeded beta band whereas beta band was higher than alpha band when participants were stressed due to their commute. Very promising results have been achieved with an accuracy of 97.5% using Feed-forward neural network. This work focuses on the development of an intelligent model that helps to predict the impact of commuting on participants. In addition, the result obtained from the Positive and Negative Affect Schedule also suggests that participants experience a considerable rise in stress after their commute. For modelling of cognitive and semantic processes underlying social behavior, the most of the recent research projects are still based on individuals, while our research focuses on approaches addressing groups as a complete cohort. This study recorded the experience of commuters with a special focus on the use and limitation of emerging computing technologies in telehealth sensors

High-wearable EEG-based distraction detection in motor rehabilitation

A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness

Machine Learning for Multi-Action Classification of Lower Limbs for BCI

Over the past two decades, significant progress has been made in brain-computer interfaces (BCIs), devices which enable direct communications between human brains and external devices. One of the prevalent control paradigms is motor imagery-based BCI (MI-BCI), by which users imagine specific actions to express their intentions. Left-hand and right-hand motor imageries are frequently used in the MI-BCI. If a third class is needed, the imagination of both feet is usually added. However, it is relatively rare to separate feet into left lower limb and right limb in MI-BCI systems. In addition, previous studies have demonstrated that real movements can be distinguished from one another via processing the electroencephalogram (EEG). Similarly, motor imagery (MI) and movement observations (MO) can also be distinguished from one another. However, classification of left lower limb actions and right lower limb actions between MI, Real Movement (RM), and MO actions, has not been thoroughly explored. To address these questions, we performed a comprehensive experiment to collect EEG under six actions (i.e., Left-MI, Right-MI, Left-RM, Right-RM, Left-MO, and Right-MO) and used three models (convolutional neural network [CNN], support vector machine [SVM], and a K-Nearest Neighbours [KNN]) to classify these actions. Our CNN achieved the highest performance (37.77%) in the classification of six actions. Although the performance of SVM (37.21%) and KNN (25.26%) was worse, it is still better than the chance level (16.67%). Our results suggest that it is possible to distinguish between these six lower limb actions. This study has implications for developing multi-class BCI systems and promoting the research of multiple-action classification