Dynamic, location-based channel selection for power consumption reduction in EEG analysis

Faul, Stephen; Marnane, William P.

doi:10.1016/j.cmpb.2012.06.005

Cited by 23 publications

(13 citation statements)

References 17 publications

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“…Location spread with twin idling is another approach for the authors, but it did not achieve an appreciable gain in terms of performance over the single idling approach with approximately 10 % drop in computational cost. The results of these approaches have been compared with the all-channel (eight channels) REACT system [14] results leading to a feasibility of the suggested approaches by Faul and Marnane [15].…”

Section: Selection Based On Entropymentioning

confidence: 99%

“…H(X;Y) is the joint entropy of the two variables X and Y. The authors evaluated their method on two patients' data, of Freiburg database, which is a publicly available intracranial EEG database containing six channels (three focal and three extrafocal) ECoG recordings of 21 patients with a 256 Hz sampling rate [15]. This method achieved 60.6 and 60 % success rates in three-channel cases of ECoG and EEG datasets, respectively.…”

Section: Selection Based On Entropymentioning

confidence: 99%

“…He reported that the computational efforts can be reduced by up to 65 % with no effect on the seizure detection performance of the REACT system (96 % for neonatal and 94 % for adult databases). Faul and Marnane [15] developed statistics-based dynamic EEG channel selection methods to reduce the power consumption in seizure detection, which can be classified as filtering techniques. In their methods, a number predefined or primary screening channels (PSCs) are chosen in the following approaches depending on the probability provided by the REACT system, and then the channels are added or removed in the next epoch.…”

Section: Selection Based On Entropymentioning

confidence: 99%

“…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)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26), and gamma band [4]. Delta waves are related to the deep sleep state.…”

Section: Introductionmentioning

confidence: 99%

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A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

297

167

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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.

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Section: Selection Based On Entropymentioning

confidence: 99%

Section: Selection Based On Entropymentioning

confidence: 99%

Section: Selection Based On Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

297

167

View full text Add to dashboard Cite

show abstract

“…Faul and Marnane have developed dynamic electrode selection method to decrease power consumption in seizure detection. They have provided power saving at 47 % without any decrease in detection performance in the study where they evaluated power consumption and detector performance by using different number of electrodes [8].…”

Section: Introductionmentioning

confidence: 99%

The Investigation of Channel Selection Effects on Epileptic Analysis of EEG Signals

Yıldız¹,

Bergil²

2015

Balkan Journal of Electrical and Computer Engineering

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A great number of methods are used in order to increase the speed of decision units in epileptic analysis of the multi-channel EEG signals. Channel selection is one of the main methods used for the reduction of the processing load. By eliminating the non-distinct channels, the performance of the system can be improved. In this study, the seizure detection performances of EEG signals obtained by 21 different channels were evaluated. This study was carried out patient-specifically for each six patients. The feature set is generated via calculating 26 features from EEG signals. The dimension of feature set for each channel is reduced using Principal Component Analysis. The reduced feature sets were divided as training and testing data using cross-validation method. With Linear Discriminant Analysis, the classification was done for each channel and performances of channel were compared. Depending on the channel selection, almost 9% differences in the classification accuracies have been observed.

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Filtering techniques for channel selection in motor imagery EEG applications: a survey

2019

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Brain Computer Interface (BCI) Systems are used in a wide range of applications such as communication, neuro-prosthetic and environmental control for disabled persons using robots and manipulators. A typical BCI system uses different types of inputs; however, Electroencephalography (EEG) signals are most widely used due to their non-invasive EEG electrodes, portability, and cost efficiency. The signals generated by the brain while performing or imagining a motor related task (Motor Imagery (MI)) signals are one of the important inputs for BCI applications. EEG data is usually recorded from more than 100 locations across the brain, so efficient channel selection algorithms are of great importance to identify optimal channels related to a particular application. The main purpose of applying channel selection is to reduce computational complexity while analysing EEG signals, improve classification accuracy by reducing over-fitting, and decrease setup time. Different channel selection evaluation algorithms such as filtering, wrapper, and hybrid methods have been used for extracting optimal channel subsets by using predefined criteria. After extensively reviewing the literature in the field of EEG channel

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Dynamic, location-based channel selection for power consumption reduction in EEG analysis

Cited by 23 publications

References 17 publications

A review of channel selection algorithms for EEG signal processing

A review of channel selection algorithms for EEG signal processing

The Investigation of Channel Selection Effects on Epileptic Analysis of EEG Signals

Filtering techniques for channel selection in motor imagery EEG applications: a survey

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