Classification of motor imagery tasks for electrocorticogram based brain-computer interface

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi

doi:10.1007/s13534-014-0128-0

Cited by 21 publications

(13 citation statements)

References 17 publications

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“…Machine learning techniques including linear discriminant analysis (LDA) [4], AdaBoost algorithm [5], k nearest-neighbors (k-NN) algorithm and Bayes classifier [6], regression trees (RT) [7], support vector machines (SVM) [8][9][10][11][12][13][14], decision trees (DT), naive Bayes (NB), and multilayer perceptron (MLP) [12] are widely employed in the design of medical decision support systems. Indeed, machine learning techniques have been commonly used in the design of computer-aided-diagnosis (CAD) systems with applications in classification of brain magnetic resonance images [15][16][17], mammograms [18,19], electroencephalography of seizures [20,21], retinal pathologies [22,23], electrocorticogram signals [24], heartbeat signals [25], and arrhythmias [26]. Several machine learning techniques have been employed for supporting the diagnosis of Parkinson's disease (PD) including SVM [1], artificial neural networks (ANN) [27,28], LDA [29], and fuzzy k-NN [30].…”

Section: Introductionmentioning

confidence: 99%

Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures

2017

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Parkinson's disease (PD) is a widespread degenerative syndrome that affects the nervous system. Its early appearing symptoms include tremor, rigidity, and vocal impairment (dysphonia). Consequently, speech indicators are important in the identification of PD based on dysphonic signs. In this regard, computer-aided-diagnosis systems based on machine learning can be useful in assisting clinicians in identifying PD patients. In this work, we evaluate the performance of machine learning based techniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were considered and trained with a set of twenty-two voice disorder measurements to classify healthy and PD patients. These machine learning methods included linear discriminant analysis (LDA), k nearest-neighbors (k-NN), naïve Bayes (NB), regression trees (RT), radial basis function neural networks (RBFNN), support vector machine (SVM), and Mahalanobis distance classifier. We evaluated the performance of these methods by means of a tenfold cross validation protocol. Experimental results show that the SVM classifier achieved higher average performance than all other classifiers in terms of overall accuracy, G-mean, and area under the curve of the receiver operating characteristic plot. The SVM classifier achieved higher performance measures than the majority of the other classifiers also in terms of sensitivity, specificity, and F-measure statistics. The LDA, k-NN and RT achieved the highest average precision. The RBFNN method yielded the highest F-measure.; however, it performed poorly in terms of other performance metrics. Finally, t tests were performed to evaluate statistical significance of the results, confirming that the SVM outperformed most of the other classifiers on the majority of performance measures. SVM is a promising method for identifying PD patients based on classification of dysphonia measurements.

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Section: Introductionmentioning

confidence: 99%

Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures

2017

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“…Since ST has an independent Gaussian window, it can realize higher frequency resolution at low frequencies and superior time domain positioning at higher frequencies. Researchers have used ST to detect dynamic EEG signals [30], [35]. The discrete Fourier transform of the discrete time series x [kT ] , k = 0, 1, .…”

Section: B Feature Extractionmentioning

confidence: 99%

Automated Selection of a Channel Subset Based on the Genetic Algorithm in a Motor Imagery Brain-Computer Interface System

Chang

Yang

2019

IEEE Access

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Brain-computer interface (BCI) is a system for communication and control between the human brain and computers or other electronic devices. However, multichannel signal acquisition, which is timeconsuming, laborious, and not conducive to subsequent real-time signal processing, can cause channel redundancy. When designing a BCI system, the selection of the optimal channels that match the expected pattern of potential cortical activity is useful for classifying brain activity during a mental task. The Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of channel selection to extract the most relevant channel for classification. The superior performance of the algorithm is demonstrated by the test results on BCI Competition III dataset I. For a motor imagery paradigm, we show that the number of used channels can be reduced significantly, and the classification performance of the classifier can be improved. By comparing the performances of the algorithm with or without channel selection, the best channel combination was selected, and only 28 out of the 64 acquisition electrodes were used to realize classification sensitivity, specificity, precision, accuracy and Kappa coefficient values of 98%, 96%, 96.08%, 97% and 0.94, respectively, thereby outperforming existing algorithms. The proposed algorithm can reduce the number of channels and select the best channel combination to improve the classification performance, thereby overcoming the redundant channel problem. In addition, the signal processing framework that is adopted by this research can serve as a reference for related BCI application system research.

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“…BCI systems based on sensorimotor rhythms are known as motor imagery (MI) BCI systems [2]. Sensorimotor rhythms include alpha (8)(9)(10)(11)(12)(13) and beta (14-26 Hz) frequency bands [3,4]. When a human imagines a motor action without any actual movement, the power of alpha and beta rhythms can decrease or increase in the sensorimotor cortices over the contralateral hemisphere and the ipsilateral hemisphere; this phenomenon is called event-related desynchronization/synchronization (ERD/ERS) [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Non-invasive techniques such as EEG have been widely used in many important BCI systems, including two-dimensional and three-dimensional BCI control [11,12]. Compared with EEG, invasive techniques such as ECoG provide superior signal quality, higher temporal and spatial resolution, broader bandwidth, higher amplitude, better signal-to-noise ratio (SNR), and lower vulnerability to artifacts such as blinks and eye movement [13]. In the second stage, the signal processing procedure converts digitized signals into commands that operate an output device [11,14,15] (e.g., industrial robot arms, wheelchairs, quadcopters).…”

Section: Introductionmentioning

confidence: 99%

Representation Learning for Motor Imagery Recognition with Deep Neural Network

Rong

Miao

et al. 2021

Electronics

Self Cite

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This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) on the brain–computer interface (BCI) system. This method is different from the traditional feature extraction and classification method. In this paper, the proposed method employs the deep learning algorithm for extracting features and the traditional algorithm for classification. Specifically, we mainly use the convolution neural network (CNN) to extract the features from the training data and then classify those features by combing with the gradient boosting (GB) algorithm. The comprehensive study with CNN and GB algorithms will profoundly help us to obtain more feature information from brain activities, enabling us to obtain the classification results from human body actions. The performance of the proposed framework has been evaluated on the dataset I of BCI Competition III. Furthermore, the combination of deep learning and traditional algorithms provides some ideas for future research with the BCI systems.

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Classification of motor imagery tasks for electrocorticogram based brain-computer interface

Cited by 21 publications

References 17 publications

Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures

Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures

Automated Selection of a Channel Subset Based on the Genetic Algorithm in a Motor Imagery Brain-Computer Interface System

Representation Learning for Motor Imagery Recognition with Deep Neural Network

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