Detection of motor imagery EEG signals employing Naïve Bayes based learning process

Siuly, Siuly; Wang, Hua; Zhang, Yanchun

doi:10.1016/j.measurement.2016.02.059

Cited by 109 publications

(47 citation statements)

References 29 publications

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“…First, the narrow window and its combination in tackling non-stationary EEG signal [24]. Second, the using of higher order statistic (skewness and kurtosis) as statistical feature extraction method [6], mean average value and root mean square [22], [34]. Finally, the using of channel instantiation approach [22] that create more instance that effective for k-NN as instance-based classifier in EEG based MI classification [6].…”

Section: B Selected Channel Classification Resultsmentioning

confidence: 99%

“…This dataset transformation based on channel instantiation that proposed by [22]. However, their proposed method only transform in class level not trial level and then perform classification.…”

Section: B Narrow Window Feature Extractionmentioning

confidence: 99%

“…Based on prior research results, CSP still need improvement since CSP very sensitive to noise in nature and often over-fit with small training sets [22]. Existing studies that employed CSP yield excellent accuracy; however, the result still not consistent where some subsets gained low accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies that employed CSP yield excellent accuracy; however, the result still not consistent where some subsets gained low accuracy. Therefore, how to improve the detection performance of EEG based MI is still a challenging issue for the development of BCI systems [22].…”

Section: Introductionmentioning

confidence: 99%

“…K-NN proven as promising classifier in EEG based MI classification [6]. Since instance-based learning need more data, we employ data transformation approach called channel instantiation where EEG multi-channel data transformed into instance which introduced by [22].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Wijaya¹,

Adji²,

Setiawan³

2018

EECSI

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Achieving consistent accuracy still big challenge in EEG based Motor Imagery classification since the nature of EEG signal is non-stationary, intra-subject and inter-subject dependent. To address this problems, we propose the feature extraction scheme employing statistical measurements in narrow window with channel instantiation approach. In this study, k-Nearest Neighbor is used and a voting scheme as final decision where the most detection in certain class will be a winner. In this channel instantiation scheme, where EEG channel become instance or record, seventeen EEG channels with motor related activity is used to reduce from 118 channels. We investigate five narrow windows combination in the proposed methods, i.e.: one, two, three, four and five windows. BCI competition III Dataset IVa is used to evaluate our proposed methods. Experimental results show that one window with all channel and a combination of five windows with reduced channel outperform all prior research with highest accuracy and lowest standard deviation. This results indicate that our proposed methods achieve consistent accuracy and promising for reliable BCI systems.

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Section: B Selected Channel Classification Resultsmentioning

confidence: 99%

Section: B Narrow Window Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Wijaya¹,

Adji²,

Setiawan³

2018

EECSI

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A novel method for EEG based automated eyes state classification using recurrence plots and machine learning approach

Khosla

Khandnor

Chand

2022

Concurrency and Computation

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Automated eyes-state classification from the EEG signals using nonlinear analysis tools is a new area of research. Based upon the theory of nonlinear analysis, recurrence plots (RPs) and recurrence quantification analysis (RQA) are of greater significance that help in understanding the chaotic and recurrence behavior of the dynamically occurring complex physiological signals. In our study, a novel method is proposed combining the RPs with the machine learning based algorithms for automated classification of EEG signals into eyes-open and eyes-close states. A huge dataset of 109 subjects has been acquired from the PhysioNet database. Each of the six RQA-based measures (recurrence rate, determinism, entropy, laminarity, trapping time, and longest vertical line)has been extracted from 64 EEG channels. Feature selection has been performed using genetic algorithm. The selected features have been averaged and combined to form a six-dimensional input vector which shows statistically significant differences (p < 0.01) between the two states. It is fed to different machine learning based algorithms such as logistic-regression, support vector machine, random forest, k-nearest neighbor, Gaussian naïve Bayes, and adaptive boosting. Logistic regression achieves the highest performance results in terms of accuracy, F1 score, precision, recall, and specificity of 97.27%, 97.17%, 98.26%, 96.36%, and 98.18%, respectively, with the least testing time of the model as 2.52 ms. Therefore, our method might be of greater significance in the development of the practical applications.

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Attempted movement classification of spinal cord injured patients using hybrid CNN‐LSTM networks

Shrestha,

Rahman,

Saha

et al. 2024

Int J Imaging Syst Tech

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Individuals who are suffering from the most severe of motor disabilities can improve their quality of life by controlling and directing mechanical and electronic devices. As for Spinal Cord Injured (SCI) patients', attempted hand movements can be classified using electroencephalography (EEG). The research aims to develop a hybrid CNN‐LSTM (Convolutional Neural Network—Long Short Term Memory) architecture for multichannel EEG signal classification. It is a challenging task to classify real‐world multichannel EEG data from SCI patients. The proposed research preprocessed the EEG data to improve the signal‐to‐noise ratio and arranged for them to extract additional information from the data. The preprocessing step includes filtering, downsampling, and artifact removal, while the postprocessing step includes time‐frequency representation and spatial information encoding. A hybrid CNN‐LSTM is used for feature extraction and classification. The proposed method has been implemented on a dataset consisting of 5 different classes of attempted hand movements from 10 SCI patients. The average classification accuracy of 92.36% is achieved for 5‐class classification. To check the global validity of the proposed network, the BCI competition IV data is classified by the proposed method and has found 92.70% overall accuracy.

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Detection of motor imagery EEG signals employing Naïve Bayes based learning process

Cited by 109 publications

References 29 publications

Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

A novel method for EEG based automated eyes state classification using recurrence plots and machine learning approach

Attempted movement classification of spinal cord injured patients using hybrid CNN‐LSTM networks

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