Classification of mental tasks from EEG data using backtracking search optimization based neural classifier

Agarwal, Saurabh; Shah, Saatvik; Kumar, Rajesh

doi:10.1016/j.neucom.2015.03.041

Cited by 41 publications

(20 citation statements)

References 35 publications

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“…• PSD features [39]. Four frequency bands are identified for interpretation of the EEG signals [40,41] but, as it is usual in BCI systems, only the most reactive frequency bands for MI have computed [39]: the alpha (8-12 Hz) and beta range (16)(17)(18)(19)(20)(21)(22)(23)(24). Then, for each EEG signal, two BP features are computed as the energies of the alpha and beta bands.…”

Section: Feature Extractionmentioning

confidence: 99%

“…There are also papers with the same MI task, published with data collected by the researchers [8,21]. Other examples of binary MI tasks available in the literature are discrimination left/right hand and tongue [22,23]; left/right hand and word generation [24]; left, right hand, foot and tongue [25]; left/right hand, foot and tongue [26]; left, right hand, both hands and both feet [27]; extension/flexion of wrist, open/close fingers [28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Testing Brain–Computer Interfaces with Airplane Pilots under New Motor Imagery Tasks

Rodríguez-Bermúdez¹,

López-Belchí²,

Girault³

2019

IJCIS

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The purpose of a brain-computer interface (BCI) is the recording of brain signals to translate them into commands. This work proposes new naturalistic and intuitive motor imagery (MI) BCI task for aircraft Pilots for a BCI System, and explore if they take advantage of their previous motor experience in MI-BCI experiments. Results show that Pilots get better performance than a control group and that it is possible to get good classification accuracy results with new naturalistic MI tasks.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Testing Brain–Computer Interfaces with Airplane Pilots under New Motor Imagery Tasks

Rodríguez-Bermúdez¹,

López-Belchí²,

Girault³

2019

IJCIS

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show abstract

“…% Topological opposition-based learning (TOBL) (10) for i � 1 : N do (11) for j � 1 : D do (12) OP ij � Low j + Up j − P ij (13) if abs(P best,j − P ij ) > abs(OP best,j − OP ij ) then (14) P ij � OP ij (15) end (16) end (17) end (18) % Selection-I (19) for a � randr; b � randrdo (20) if a < b then (21) OldP � P (22) end (23) end (24) OldP � permuting(OldP) (25) % Improved mutation (26) for i � 1 : Ndo (27) IMut (43) end (44) end (45) end (46) % Boundary control mechanism (47) for…”

Section: Contrastive Analysis Between Mbsagc and Bsamentioning

confidence: 99%

“…ese advantages make it become a promising newcomer among evolutionary algorithms. And so far, it has been successfully applied to digital image processing [15,16], power systems [17][18][19], energy and environmental management [20][21][22], artificial neural networks [23][24][25][26], induction motor [27,28], antenna arrays [29,30], and so on.…”

Section: Introductionmentioning

confidence: 99%

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

Liu

Miao³

et al. 2019

Mathematical Problems in Engineering

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The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-tendency of collective wisdom. The topological opposition-based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity-tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10-dimension and 30-dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state-of-the-art algorithms.

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“…The former may be approached using such methods as common spatial pattern (CSP), extreme energy ratio (ERR), orthogonal parametric transforms [ 17 ], autoregressive (AR) parameters, wavelet packet transform (WPT) [ 18 ], principal component analysis (PCA or KLT) [ 19 , 20 ] or hidden Markov model (HMM) to carry out dimensionality reduction [ 21 ]. All aforementioned methods are constantly modified to meet the growing usability and effectiveness requirements.…”

Section: State Of the Artmentioning

confidence: 99%

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Opałka

Stasiak

Szajerman

et al. 2018

Sensors

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Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

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Classification of mental tasks from EEG data using backtracking search optimization based neural classifier

Cited by 41 publications

References 35 publications

Testing Brain–Computer Interfaces with Airplane Pilots under New Motor Imagery Tasks

Testing Brain–Computer Interfaces with Airplane Pilots under New Motor Imagery Tasks

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

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