An EEG-based machine learning model using Random Forest and Gradient Boosting successfully distinguished resting, motor, and cognitive tasks, with EEG spectral features explained by LIME.
Observational (n=75)
Incorporating eXplainable Artificial Intelligence (XAI) into EEG-based Human Activity Recognition (HAR) models can accurately classify activities and provide clinical interpretability for the most influential EEG features.
Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitoring the neurological responses during cognitive and motor tasks. Machine learning (ML) represents a promising tool for the recognition of human activities (HAR), and eXplainable artificial intelligence (XAI) can elucidate the role of EEG features in ML-based HAR models. The primary objective of this investigation is to investigate the feasibility of an EEG-based ML model for categorizing everyday activities, such as resting, motor, and cognitive tasks, and interpreting models clinically through XAI techniques to explicate the EEG features that contribute the most to different HAR states. The study involved an examination of 75 healthy individuals with no prior diagnosis of neurological disorders. EEG recordings were obtained during the resting state, as well as two motor control states (walking and working tasks), and a cognition state (reading task). Electrodes were placed in specific regions of the brain, including the frontal, central, temporal, and occipital lobes (Fz, C1, C2, T7, T8, Oz). Several ML models were trained using EEG data for activity recognition and LIME (Local Interpretable Model-Agnostic Explanations) was employed for interpreting clinically the most influential EEG spectral features in HAR models. The classification results of the HAR models, particularly the Random Forest and Gradient Boosting models, demonstrated outstanding performances in distinguishing the analyzed human activities. The ML models exhibited alignment with EEG spectral bands in the recognition of human activity, a finding supported by the XAI explanations. To sum up, incorporating eXplainable Artificial Intelligence (XAI) into Human Activity Recognition (HAR) studies may improve activity monitoring for patient recovery, motor imagery, the healthcare metaverse, and clinical virtual reality settings.
Hussain et al. (Sun,) conducted a observational in Healthy individuals (n=75). EEG-based Machine Learning model with LIME was evaluated on Categorization of everyday activities (resting, motor, and cognitive tasks). An EEG-based machine learning model using Random Forest and Gradient Boosting successfully distinguished resting, motor, and cognitive tasks, with EEG spectral features explained by LIME.