An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

Belwafi, Kais; Djemal, Ridha; Ghaffari, Fakhreddine; Romain, Olivier

doi:10.1109/ccmb.2014.7020704

Cited by 20 publications

(16 citation statements)

References 22 publications

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“…The filtering block aims to remove artifacts, improve the stationary, and increase accuracy. Many alternatives have been explored in [14] as follows:The first one is using frequency domain transforms such as fast Fourier transform (FFT) or using time-frequency domain such as discrete wavelet transform (DWT).Subtracting artifacts from the acquired signal: this technique requires an average artifacts template estimation to be subtracted from the original EEG signal.Using the same static filtering for all subjects like finite impulse response (FIR) and infinite impulse response (IIR) filters: FIR filters like Equiripple and Kaiserwin are based on Parks-McClellan algorithm using the Remez exchange algorithm and Chebyshev approximation theory to design filters with an optimal_t between the desired and the actual frequency responses [15]. …”

Section: Methodsmentioning

confidence: 99%

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

Djemal

AlSharabi

Ibrahim

et al. 2017

BioMed Research International

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Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

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Section: Methodsmentioning

confidence: 99%

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

Djemal

AlSharabi

Ibrahim

et al. 2017

BioMed Research International

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“…Signal processing was performed to keep just the frequencies related to left and right hand motor imagery which are represented by µ-rhythm and β-rhythm (Lotte and Guan, 2011). Recently, a new approach is proposed to select automatically the best filters parameters that guarantee the removal of all unwanted signals, and adapts to the intrinsic individual characteristics of EEG signals for each person (Belwafi et al, 2014). This method is based on the variation of the Signal-to-Noise Ratio (SNR) on the stop band that has an explicitly effect on the pass band frequencies.…”

Section: Methodsmentioning

confidence: 99%

“…The acquired EEG signal are processed to remove all unwanted signals. These undesired frequencies are removed based on adaptive filters due to the intrinsic variability of EEG signals in each subject (Belwafi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Online Adaptive Filters to Classify Left and Right Hand Motor Imagery

Belwafi

Djemal

Ghaffari

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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Sensorimotor rhythms (SMRs) caused by motor imagery are key issues for subject with severe disabilities when controlling home devices. However, the development of such EEG-based control system requires a great effort to reach a high accuracy in real-time. Furthermore, BCIs have to confront with inter-individual variability, imposing to the parameters of the methods to be adapted to each subjects. In this paper, we propose a novel EEG-based solution to classify right and left hands(RH and LH) thoughts. Our approach integrates adaptive filtering techniques customized for each subject during the training phase to increase the accuracy of the proposed system. The validation of the proposed architecture is conducted using existing data sets provided by BCI-competition and then using our own on-line validation platform experienced with four subjects. Common Spatial Pattern (CSP) is used for feature extraction to extract features vector from µ and β bands. These features are classified by the Linear Discriminant Analysis (LDA) algorithm. Our prototype integrates the Open-BCI acquisition system with 8 channels connected to Matlab environment in which we integrated all EEG signal processing including the adaptive filtering. The proposed system achieves 80.5% of classification accuracy, which makes approach a promising method to control an external devices based on the thought of LH and RH movement.

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“…These frequency components were often between 8 Hz and 30 Hz [23]. Thus, a finite impulse response filter was applied with a 4 th order allowing the removal of frequency components outside the band while maintaining a zeros frequency phase for the signal [24].…”

Section: Artifacts Removalmentioning

confidence: 99%

“…One-versus-rest approach is applied in order to prepare the features related to each finger movement. These features represent in reality the most significant energy at α and β bands; which are the most likely to contain significant motor imagery information [23]. Furthermore, the feature extraction method allows to offload the classifier work and facilitate discrimination between classes.…”

Section: Selection Of Relevant Electrodesmentioning

confidence: 99%

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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An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

Cited by 20 publications

References 22 publications

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

Online Adaptive Filters to Classify Left and Right Hand Motor Imagery

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

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