A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals

Abstract

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?

Multimedia implicit tagging using EEG signals

“…The response to the relevant tags looks more like P300 response which is associated with the target tag in participant's mind. P300 responses are used for BCI spellers [31]. The response to the irrelevant tags is more like a delayed N400 response associated with mismatch.…”

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies

“…A. Robinson 1982). This frequency smoothing effect produces accurate estimates of broadband signals, such as gamma frequencies in motor related activity (Bashashati et al 2007), but poor estimates of narrowband signals. Wavelets are used to help remove the timingfrequency resolution trade-offs for signals with specific and known characteristics.…”

Signal Processing and Classification Approaches for Brain-Computer Interface

“…For an extensive description and tutorials on these and other approaches, refer to (Lotte et al, 2007;Bashashati et al, 2007;Hung et al, 2005).…”