Survey on Brain-Computer Interface: An Emerging Computational Intelligence Paradigm

摘要

A brain-computer interface (BCI) provides a way to develop interaction between a brain and a computer. The communication is developed as a result of neural responses generated in the brain because of motor movements or cognitive activities. The means of communication here includes muscular and non-muscular actions. These actions generate brain activities or brain waves that are directed to a hardware device to perform a specific task. BCI initially was developed as the communication device for patients suffering from neuromuscular disorders. Owing to recent advancements in BCI devices-such as passive electrodes, wireless headsets, adaptive software, and decreased costs-it is also being used for developing communication between the general public. The BCI device records brain responses using various invasive and non-invasive acquisition techniques such as electrocorticography (ECoG), electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI). In this article, a survey on these techniques has been provided. The brain response needs to be translated using machine learning and pattern recognition methods to control any application. A brief review of various existing feature extraction techniques and classification algorithms applied on data recorded from the brain has been included in this article. A significant comparative analysis of popular existing BCI techniques is presented and possible future directives are provided.