Motor imagery EEG classification: study case of unhealthy and healthy subjects

Since the introduction of Electroencephalogram (EEG), its signals have been vital for diagnosing neurological issues such as epilepsy and other problems. Brain-computer interfaces (BCIs) have transformed mind-controlled robotics, with the integration of Deep Learning that enhances EEG signal decoding. However, factors like substance abuse and Parkinson’s disease pose classification challenges. To address these complexities, our research designs and validates a dual-model architecture that generalizes motor imagery classification for mind-controlled robots, encompassing both healthy individuals and those affected by substance addiction. In addition, this study assesses the real-world applications of these models in piloting advanced robotic platforms, including quad-rotors and wheeled mobile robots. In one hand, our empirical evaluation highlighted clear distinctions between the SVM and attention-based Bi- LSTM models; With the SVM, precision values fluctuated within a range of 0.702 to 0.782, while recall metrics varied between 0.705 and 0.790. On the other hand, the attention-based Bi-LSTM model demonstrated a broader precision range of 0.744 to 0.864 and recall values spanning from 0.752 to 0.856. Impressively, the average precision and recall for the Bi-LSTM model stood at 0.807 and 0.809 respectively, indicating its robust and consistent performance. The evident proficiency of this model in classifying EEG signals ushers in an innovative and inclusive approach to mind-controlled tasks.