A review of channel selection algorithms for EEG signal processing

By Turky Nayef Alotaiby, Fathi E. Abd El-Samie, Saleh A. Alshebeili, Ishtiaq Ahmad. EURASIP Journal on Advances in Signal Processing, 2015

434 citations recorded by the source corpus.

Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9e5bfcae060393fb4822504cdb04897c1de214ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Apiwat Lek-uthai
2. Poomipat Boonyakitanont
3. Jitkomut Songsiri
4. Krisnachai Chomtho

In Signal Processing

1. Apiwat Lek-uthai
2. Poomipat Boonyakitanont
3. Jitkomut Songsiri
4. Krisnachai Chomtho

Citations in the corpus

• Emotions Recognition Using EEG Signals: A Survey (2019, neutral): “Adapted from [17] (best seen in color).”
• A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, background): “Studies on channel selection techniques for processing EEG signals and reducing feature dimensions in seizure detection and prediction are needed because considering every channel may cause overfitting problems [AESAA15].”