In addition, it adds to the computational complexity of the system, which is required to be low in certain applications.Īnother example where channel reduction is of a potential value is in seizure detection and prediction. In this signal processing setting, reducing the number of channels is needed because the setup process with a large number of channels is time-consuming and causes subject inconvenience. Figure 4 gives an illustration for the general process of EEG signal classification based on channel selection. To classify these signals, for example, we have two choices: to work on a subset of channels selected based on certain criteria or to work on all channels. The acquired EEG signals are generally of multi-channel nature. When dealing with mental illnesses states, unexpected disturbances of the brain waves occur leading to the need of considerable signal processing burdens for diagnosis of abnormal states. Gamma waves are highly related to the decision-making mode of the brain. Beta waves are the dominant with the waking state with large attention. Alpha waves are related to the case of dreaming and relaxation. Theta waves are related to the deepest state of mediation (body asleep/mind awake). Delta waves are related to the deep sleep state. These frequency bands are delta band (0–4 Hz), theta band (3.5–7.5 Hz), alpha band (7.5–13 Hz), beta band (13–26 Hz), and gamma band (26–70 Hz). Most of the useful information about the functional state of a human brain lies in five major brain waves distinguished by their different frequency bands. These electrodes (channels) show the activities of different brain areas. Figure 2 shows the 10–20 EEG electrode positions for the placement of electrodes from the left and the top of the head. An electrode placement scheme on scalp, known as International 10–20 system, was recommended by the International Federation of Societies for Electroencephalography and Clinical Neurophysiology (IFSECN). On the other hand, in the bipolar mode, the voltage differences between two specified electrodes are recorded, where each pair forms a channel. In the former mode, the voltage differences between all electrodes and a reference one are recorded, where a channel is formed by an electrode-reference pair.
![eeg definition eeg definition](https://image.slidesharecdn.com/eegncvemg-181104072043/95/eeg-interpretation-2-638.jpg)
The scalp EEG signals can be recorded by different modes such as unipolar and bipolar modes.
![eeg definition eeg definition](https://wirtschaftslexikon.gabler.de/sites/default/files/graph/compact/erneuerbare-energien-gesetz-eeg-36812.png)
Scalp EEG acquisition devices are generally preferred due to their low-cost, ease of use, portability, and high temporal resolution. Although invasive technologies have recently shown some promises in different applications for their large accuracy and low noise, non-invasive technologies are still used extensively for safety purposes with some additional signal processing tasks to compensate for the noise and resolution limitations. 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.Īs mentioned above, the interface between the brain and the computer (or a device) could be through invasive or non-invasive technologies. 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. 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. 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. 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. 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.