Advances in Brain-Machine Interfaces: Utilizing EEG Signals for Mental Task Classification and Beyond

Abstract

Brain-machine interfaces (BMIs) allow direct communication between the brain and external devices, enabling people with motor impairments to control prosthetics and computers. A major focus in BMI research is decoding intended movements from neural signals to enact device control. Electroencephalography (EEG) is a popular non-invasive method to record brain activity for BMIs. Recently, EEG-based BMIs have expanded beyond device control to applications including detecting cognitive states, emotions, and speech. This article reviews key advances in EEG-based BMIs over the past decade. We first provide background on neural signal acquisition and processing. Next, we discuss advances in EEG decoding for mental task classification, highlighting shifts to deep learning and recurrent neural network approaches. We then survey emerging real-world applications of EEG-based BMIs, including augmented and virtual reality systems, adaptive automation, and passive brain-computer interfaces. Throughout, we emphasize breakthrough studies that move EEG BMIs out of controlled lab settings. We also critically analyze key challenges that remain in translating EEG BMIs to practical use. These include non-stationarity in EEG signals, individual differences, limited input information, and deficiencies in evaluation practices. We suggest future directions like longitudinal learning, explainable models, multimodal integration, and benchmarking to overcome these barriers. Our review synthesizes recent progress and persistent gaps for EEG-based BMIs, providing insights to guide further development of these emerging neurotechnology’s.