Artificial Intelligence as a Decision Layer in Brain-Computer Interface-Controlled Exoskeletons for Rehabilitation and Human Augmentation

Abstract

A brain-computer interface (BCI) can detect neural activity related to movement, while a powered exoskeleton can provide the force needed to assist that movement. Electroencephalography (EEG) is noisy, muscle activity changes with fatigue and recovery, and the correct amount of assistance depends heavily on the person and the task, so artificial intelligence is useful mainly because it can work with several of these changing signals at once rather than relying on one fixed input. This paper reviews how EEG, electromyography (EMG), and mechanical sensing can be combined in exoskeleton control and considers two applications with very different goals. In stroke rehabilitation, robotic assistance should remain connected to the patient's own movement attempt and should decrease as voluntary control improves. In healthy users, assistance is useful when it reduces the energetic or muscular cost of a task. Recent studies have applied deep learning, transfer learning, hybrid EEG-EMG control, and human-in-the-loop optimization to these problems. The results are promising, but they also show that improving classification accuracy alone is not enough. Latency, calibration, fatigue, uncertainty, and physical safety all affect whether a system is actually useful. A practical design would therefore use AI mainly to interpret the user and choose a high-level assistance strategy, while conventional controllers continue to enforce torque, joint, velocity, and other mechanical limits.