EEG-based BCI system for decoding finger movements within the same hand

Alazrai, Rami; Alwanni, Hisham; Daoud, Mohammad I.

doi:10.1016/j.neulet.2018.12.045

Cited by 67 publications

(49 citation statements)

References 18 publications

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“…This can be attributed to several properties that are associated with the EEG modality, including the high temporal resolution, low-cost, and noninvasive nature [5,6]. Nonetheless, pain detection based on EEG signals analysis is considered challenging due to the nonstationarity nature of the EEG signals, low spatial resolution, and low signal-to-noise (SNR) ratio [7].…”

Section: Introductionmentioning

confidence: 99%

Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study

et al. 2019

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Detecting pain based on analyzing electroencephalography (EEG) signals can enhance the ability of caregivers to characterize and manage clinical pain. However, the subjective nature of pain and the nonstationarity of EEG signals increase the difficulty of pain detection using EEG signals analysis. In this work, we present an EEG-based pain detection approach that analyzes the EEG signals using a quadratic time-frequency distribution, namely the Choi–Williams distribution (CWD). The use of the CWD enables construction of a time-frequency representation (TFR) of the EEG signals to characterize the time-varying spectral components of the EEG signals. The TFR of the EEG signals is analyzed to extract 12 time-frequency features for pain detection. These features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To evaluate the performance of our proposed approach, we have recorded EEG signals for 24 healthy subjects under tonic cold pain stimulus. Moreover, we have developed two performance evaluation procedures—channel- and feature-based evaluation procedures—to study the effect of the utilized EEG channels and time-frequency features on the accuracy of pain detection. The experimental results show that our proposed approach achieved an average classification accuracy of 89.24% in distinguishing between the no-pain and pain classes. In addition, the classification performance achieved using our proposed approach outperforms the classification results reported in several existing EEG-based pain detection approaches.

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Section: Introductionmentioning

confidence: 99%

Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study

et al. 2019

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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

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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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“…Most of the studies reported in the literature focused on sensor-based BCIs. First, raw sensor data are filtered into two groups in this study: 0.1-30 Hz and µ rhythm (8)(9)(10)(11)(12)(13). Table 2 lists the classification success of sensor data for 118 channels.…”

Section: Resultsmentioning

confidence: 99%

“…In 2018, Li et al [7] studied motor imagery tasks with the same dataset used in this work and showed that source domain analysis outperforms sensor domain analysis. Alazrai et al [8] reported success in finger movements, Lu et al [9] controlled a vehicle using EEG signals, and Xygonakis et al [10], in 2018, studied four-class motor imagery in the EEG source space and improved its accuracy compared to the sensor data analysis. Qingsong et al [11] analyzed four-task motor imagery in 2019, while Zhang et al [12] reported in 2019 that children can successfully use BCIs.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery

2019

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Brain-computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1-30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery.

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EEG-based BCI system for decoding finger movements within the same hand

Cited by 67 publications

References 18 publications

Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study

Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study

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

A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery

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