Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

Anam, Khairul; Nuh, Mohammad; Al-Jumaily, Adel

doi:10.23919/eecsi48112.2019.8977037

Cited by 16 publications

(20 citation statements)

References 17 publications

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“…The proposed finger decoding system outperforms those of the previous studies, e.g., the average accuracy herein increased by 4% compared to the best previous system presented in [14]. Moreover, the proposed system significantly improves the runtime using a robust and efficient algorithms, contrary to the method presented in [14,11,12,22]. classifier was trained on a portion of the data set and tested using another portion.…”

Section: Discussionmentioning

confidence: 86%

EEG-Based BCI System to Control Prosthesis's Finger Movements

Gannouni¹,

Belwafi²,

Aboalsamh³

et al. 2020

Preprint

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Background: The advances in assistive technologies will go a long way towards restoring the mobility of paralyzed and/or amputated limbs. In this paper, we propose a system that adopts the brain-computer interface ( BCI ) technology to control prosthetic fingers by thoughts. To predict the movements of each finger, a complex EEG signal processing algorithms should be applied in order to remove the outliers , to extract feature, to discriminate between the fingers and to control prosthesis's finger. The proposed method discriminates between the five human fingers. So a multi -classification problem based on ensemble of one class-classifier is applied where each classifier predicts the intention to move one finger. At the end, an adapted machine learning strategy is proposed to predict movements of multiple fingers at the same time. Results: The sensitive regions of the brain related to finger movements are identified and located. The proposed EEG signal processing chain, based on ensemble of one class-classifier, reach a classification accuracy of 81 \ % for five subjects according to the online approach. Unlike most of the existing prototypes that allow to control only one single finger and to perform only one movement at a time by the dedicated finger, our proposed system will enable multiple fingers to perform movements simultaneously. Despite that the proposed system classifies a five tasks, the obtained accuracy is too high compared to a binary classification system. Conclusion: The proposed system contributes to the advancement of a prosthetic allowing people with severe disabilities to do the daily tasks easily.

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Section: Discussionmentioning

confidence: 86%

EEG-Based BCI System to Control Prosthesis's Finger Movements

Gannouni¹,

Belwafi²,

Aboalsamh³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, the average accuracy described herein increased by 4% compared to the best known previous system that was presented in [10]. Moreover, the proposed system significantly improves the runtime using robust and efficient algorithms in contrast with the method presented in [7][8][9][10]. A small number of trials for every finger movement are sufficient to train the system for the on-line scenario before being used by a new user.…”

Section: Discussionmentioning

confidence: 88%

“…Different types of bio-signals such as EEG [7][8][9][10], Magnetoencephalography (MEG) [11,12], Electrocorticogram (ECoG) [13][14][15], Functional Near-Infrared Spectroscopy (fNIRS) [16], as well as Electromyography (EMG) [17] have been used to design BCI systems that decode finger movements. EEG is the most commonly used technology for the acquisition of brain signals in BCI systems due to its noninvasive nature, low cost, and high portability [8].…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [9] proposed another system that applies the common spatial pattern (CSP) algorithm to extract features, as well as four classifiers, such as the random forest, SVM, k-nearest neighborhood (kNN), and the linear discriminant analysis (LDA) to discriminate between trials. The maximum average accuracy reached by these classifiers is about 54%.…”

Section: Introductionmentioning

confidence: 99%

“…Previous EEG-based studies [7][8][9][10] were usually extracting features from the same group of electrodes for all subjects and for all fingers movements, whereas neuroscience studies mentioned that the activity is unique in the brain of each person. Moreover, the brain activity is different in the left and right brain hemispheres during the same motor imagery [7,21].…”

Section: Introductionmentioning

confidence: 99%

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EEG-Based BCI System to Detect Fingers Movements

et al. 2020

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The advancement of assistive technologies toward the restoration of the mobility of paralyzed and/or amputated limbs will go a long way. Herein, we propose a system that adopts the brain-computer interface technology to control prosthetic fingers with the use of brain signals. To predict the movements of each finger, complex electroencephalogram (EEG) signal processing algorithms should be applied to remove the outliers, extract features, and be able to handle separately the five human fingers. The proposed method deals with a multi-class classification problem. Our machine learning strategy to solve this problem is built on an ensemble of one-class classifiers, each of which is dedicated to the prediction of the intention to move a specific finger. Regions of the brain that are sensitive to the movements of the fingers are identified and located. The average accuracy of the proposed EEG signal processing chain reached 81% for five subjects. Unlike the majority of existing prototypes that allow only one single finger to be controlled and only one movement to be performed at a time, the system proposed will enable multiple fingers to perform movements simultaneously. Although the proposed system classifies five tasks, the obtained accuracy is too high compared with a binary classification system. The proposed system contributes to the advancement of a novel prosthetic solution that allows people with severe disabilities to perform daily tasks in an easy manner.

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