Selecting better EEG channels for classification of mental tasks

Tavakolian, Kouhyar; Nasrabadi, Ali Motie; Rezaei, Siamak

doi:10.1109/iscas.2004.1328802

Cited by 16 publications

(11 citation statements)

References 3 publications

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“…Tavakolian et al [74] presented a channel reduction method for classifying three mental tasks (baseline, multiplication, and geometric figure rotation) based on genetic algorithms for subset generation as shown in Fig. 16.…”

Section: Wrapper Techniquesmentioning

confidence: 99%

“…For wrapper, hybrid, and embedded channel selection techniques, the performance Fig. 16 Tavakolian et al method [74] sensitivity should be studied with different types of classifiers. With channel selection, we may still work on a multi-channel basis, so the development of a framework containing channel selection and decision fusion is an open area for further investigation.…”

Section: Conclusion and Future Research Directionsmentioning

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.

351

181

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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: Wrapper Techniquesmentioning

confidence: 99%

Section: Conclusion and Future Research Directionsmentioning

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

“…They also compare feed forward neural network, Bayesian quadratic, Bayesian network, Fisher linear classifier and Hidden Markov Models for EEG signal classification. Since these classifiers cover both linear and nonlinear methods, a comparison between them can lead us to a quite comprehensive conclusion [23]. In general Bayesian quadratic classifier performed better than Bayesian network.…”

Section: Classification and Feature Extraction Methodsmentioning

confidence: 99%

EEG signal classification for real-time brain-computer interface applications: A review

Khorshidtalab¹,

Salami²

2011

2011 4th International Conference on Mechatronics (ICOM)

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Brain-computer interface (BCI) islinking the brain activity to computer, which allows a person to control devices directly with his brain waves and without any use of his muscles. Recent advances in real-time signal processing have made BCI a feasible alternative for controlling robot and for communication as well. Controlling devices using BCI is a crucial aid for people suffering from severe disabilities and more than that, BCIs can replace human to control robots working in dangerous or uncongenial situations. Effective BCIs demand for accurate and real-time EEG signals processing. This paper is to review the current state of research and to compare the performance of different algorithms for real-time classification of BCIbased electroencephalogram signals.

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“…This method provides fully automated detection and quantification of ERP components that best discriminate between two samples of EEG signals. A new method is also proposed in [19] [20], Self organizing Feature Map [21], Multiple Kernel Learning Support Vector Machine [22], neural networks with improved particle swarm optimization [23], recurrent neural networks [24], time-frequency analysis [25], Fuzzy sets based classification [26] and Independent Component Analysis (ICA) [27]. This paper presents a novel but a very simple method to effectively classify the EEG of mental tasks for left-hand movement imagination, right-hand movement imagination and word generation.…”

Section: Imagination Of Movements Is Called Sensory Motor Rhythm (Smrmentioning

confidence: 99%

Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks

Bawane¹,

Bhurchandi²

2016

IJCA

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With recent advances in Electroencephalogram (EEG) signal processing and biomedical instrumentation, brain machine interfaces are used for rehabilitation of people suffering from neuromuscular disorders. This paper presents a novel method employing Hierarchical classifier using optimised Neural Networks to classify left-hand movement, right-hand movement and word generation using EEG signals. One of the most important components of brain computer interface (BCI) is feature extraction of EEG signals. Power spectral density (PSD) is used for feature extraction from EEG signals. The proposed pre-processing and reconfiguration of PSD samples make them more discriminative & yield appropriately organized feature vectors. The adaptation of network weights using Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed to improve the performance of Neural Network (NN). Further, the two level hierarchical neural network is used to enhance the discriminative property of the features and hence better classification accuracy is achieved. Results are verified on BCI benchmarking database as well as our own experimental database. Results obtained using the proposed methods are compared with other contemporary methods such as Linear Discrimination analysis (LDA), neural networks based on improved particle swarm optimization (IPSONN) and to a recently proposed approach based on Evidence-based combining classifier. It is found that the proposed method outperforms all the contemporary techniques for the multi-task EEG classification. This new method can be easily extended to other multitask BCI applications.

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Selecting better EEG channels for classification of mental tasks

Cited by 16 publications

References 3 publications

A review of channel selection algorithms for EEG signal processing

A review of channel selection algorithms for EEG signal processing

EEG signal classification for real-time brain-computer interface applications: A review

Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks

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