Optimal combination of channels selection based on common spatial pattern algorithm

Li, Mingai; Ma, Jianyong; Jia, Songmin

doi:10.1109/icma.2011.5985673

Cited by 18 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Li et al [67] proposed a method for selecting suitable channels for classification of two motor imagery tasks: right hand and right foot based on a common spatial pattern algorithm as shown in Fig. 12.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

“…The method of Rizon's et al is a filtering approach with a pre-specified subset of channels selected by a Fig. 12 Flowchart of Li et al algorithm [67] human expert. It was evaluated using 63 channel EEG recordings (28 pairs, seven center electrodes) from five healthy subjects with a 256 Hz sampling rate and a band-pass filter between 0.05 and 70 Hz with five different classes of emotions (disgust, happy, surprise, sad, and fear).…”

Section: Channel Selection For Emotion Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

351

181

View full text Add to dashboard Cite

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.

show abstract

Section: Hybrid Techniquesmentioning

confidence: 99%

Section: Channel Selection For Emotion Classificationmentioning

confidence: 99%

A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

351

181

View full text Add to dashboard Cite

show abstract

“…The embedded method facilitates the interaction between channel selection and classification. The hybrid method is a combination of the filtering and wrapper techniques [40]. The human-based method uses humans in the feedback task for channel selection.…”

Section: Methodsmentioning

confidence: 99%

Energy extraction method for EEG channel selection

Fauzi

Azzam²,

Shapiai

et al. 2019

TELKOMNIKA

View full text Add to dashboard Cite

Channel selection is an improvement technique to optimize EEG-based BCI performance.In previous studies, many channel selection methods-mostly based on spatial information of signals-have been introduced. One of these channel selection techniques is the energy calculation method. In this paper, we introduce an energy optimization calculation method, called the energy extraction method. Energy extraction is an extension of the energy calculation method, and is divided into two steps. The first step is energy calculation and the second is energy selection. In the energy calculation step, l2-norm is used to calculate channel energy, while in the energy selection method we propose three techniques: "high value" (HV), "close to mean" (CM), and "automatic". All proposed framework schemes for energy extraction are applied in two types of datasets. Two classes of datasets i.e. motor movement (hand and foot movement) and motor imagery (imagination of left-and right-hand movement) were used. The system used a Common Spatial Pattern (CSP) method to extract EEG signal features and k-NN as a classification method to classify the signal features with k=3. Based on the test results, all schemes for the proposed energy extraction method yielded improved BCI performance of up to 58%. In summary, the energy extraction approach using the CM energy selection method was found to be the best channel selection technique.

show abstract

“…The quality of results from a mining algorithm offers a natural stopping criterion [25]. Li et al [61] used the L1 norm of CSP to first sort out the best channels and used the classification accuracy as an optimization function. With this method, an average accuracy of 90% was achieved with only 7 channels.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

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

show abstract

Optimal combination of channels selection based on common spatial pattern algorithm

Cited by 18 publications

References 11 publications

A review of channel selection algorithms for EEG signal processing

A review of channel selection algorithms for EEG signal processing

Energy extraction method for EEG channel selection

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

Contact Info

Product

Resources

About