Chapter 10 - Statistical Pattern Recognition and Machine Learning in Brain–Computer Interfaces

Abstract

The chapter provides an over view of some of the major feature extraction, pattern recognition, and machine learning techniques that have been successfully applied to electroencephalography (EEG)- and ECoG-based Brain–computer interfaces (BCIs). BCIs augment the human ability to communicate and interact with the external world by directly linking the brain to computers and robotic devices. BCIs bypass the normal neuromuscular output pathways for translating brain signals into action. Instead, physiological brain signals are processed in real time by digital signal processing methods to allow a novel form of communication and interaction with the environment. A major goal of BCIs has been to improve the quality of life of physically impaired individuals, including those paralyzed because of degenerative neurological diseases such as amyotrophic lateral sclerosis, spinal cord injury, or stroke. BCIs allow a subject to directly control objects such as a cursor or a robot using brain signals. BCIs depend crucially on the ability to reliably identify behaviorally induced changes (or “cognitive states”) in the brain signals being recorded. Statistical pattern recognition and machine learning algorithms play an important role in identifying these changes in brain signals and mapping them to appropriate control signals.