A review of signal processing in brain computer interface system

An incremental framework for classification of EEG signals using quantum particle swarm optimization

“…Also, the trajectory of a particle is modeled using quantum Delta potential well model. In this model, it is assumed that a particle moves in a Delta potential well in search space, of which the center is point p calculated by (4). In order to compute the fitness of an individual particle, its exact position is needed.…”

“…x pbi is the best personal position that ith particle have seen so far, and x g is the position of the best particle seen so far in the swarm history. It has been shown that if the upper limits of personal and social cognitive parameters are selected properly, the position of the ith particle converges to position p computed as (4). Position p can be interpreted as center of gravity towards which particles are careen while their kinetic energy declines [37].…”

“…Finally, a control interface interprets the classified signals into high level commands and provides connected devices with those comprehendible commands. Further information regarding signal processing techniques in BCI systems can be found in [4,5].…”

Integrating Neural Signal and Embedded System for Controlling Small Motor

“…The FFT only gives frequency information of the signal, thus, time-frequency analysis or spectrogram is normally used to view the frequency at each time point. [Norani et al, 2010] The next stage is extracting the underlying information in the signal. Depending on the purpose of the study, this stage can be feature extraction or event detection as shown in Figure 7.…”

Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set

“…It is an emerging technique nowadays and provide a communication facility to control and actuate devices using brain signals [1]. Various techniques have been adopted to extract signals from brain which includes magneto electroencephalography (MEG), Functional magnetic resonance imaging 1 The research work is supported by Department of Mechatronics Engineering (College of Electrical & Mechanical Engineering) (fMRI), near infrared spectroscopy (NIRS), electrocarticogram (ECoG) and electroencephalography (EEG) [1] & [2]. Among these aforementioned techniques signals acquisition using EEG is a rapid infusion in BCI since it reflects the electrical responses of human brain in actions and it is widely used because of its noninvasiveness, higher temporal resolution, Inexpensiveness, and no exposure to radiations.…”