User-customized brain computer interfaces using Bayesian optimization

Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali

doi:10.1088/1741-2560/13/2/026001

Cited by 38 publications

(30 citation statements)

References 38 publications

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“…To assert the performances of our method, we compare with FBCSP, Riemannian geometry [4], Bayesian optimization [5], and ShallowNet [17]. Table 1 shows the results of the classification of our method and the baselines in terms of accuracy.…”

Section: Resultsmentioning

confidence: 99%

Incep-EEGNet: A ConvNet for Motor Imagery Decoding

Riyad¹,

Adib²

2020

Lecture Notes in Computer Science

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The brain-computer interface consists of connecting the brain with machines using the brainwaves as a mean of communication for several applications that help to improve human life. Unfortunately, Electroencephalography that is mainly used to measure brain activities produces noisy, non-linear and non-stationary signals that weaken the performances of Common Spatial Pattern (CSP) techniques. As a solution, deep learning waives the drawbacks of the traditional techniques, but it still not used properly. In this paper, we propose a new approach based on Convolutional Neural Networks (ConvNets) that decodes the raw signal to achieve state-of-the-art performances using an architecture based on Inception. The obtained results show that our method outperforms state-of-the-art filter bank common spatial patterns (FBCSP) and ShallowConvNet on based on the dataset IIa of the BCI Competition IV.

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Section: Resultsmentioning

confidence: 99%

Incep-EEGNet: A ConvNet for Motor Imagery Decoding

Riyad¹,

Adib²

2020

Lecture Notes in Computer Science

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“…SVM-FBCSP is an improved version of FBCSP, which recommended the Hilbert transform to extract the envelope of the signal, and then chose SVM to classify the features [ 42 ]. BO is a method to optimize hyperparameters which used Bayesian optimization on the basis of extracting CSP features [ 43 ]. Compared with the three algorithms, for the seventh subject, the classification accuracy of the SVM-FBCSP is better than our method, but the average accuracy of our algorithm is 10.7574% higher than FBCSP, 10.3774% higher than BO, and 7.3274% higher than SVM-FBCSP.…”

Section: Resultsmentioning

confidence: 99%

Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Zheng

Wang

et al. 2020

Sensors

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Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.

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“…When subject-specific maximized performance is desired, these time intervals could be determined automatically (see e.g. Bashashati et al 2016). As expected, the standard deviation is clearly reduced in the baseline interval.…”

Section: Data Preprocessingmentioning

confidence: 94%

Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

2020

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Electroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.

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User-customized brain computer interfaces using Bayesian optimization

Cited by 38 publications

References 38 publications

Incep-EEGNet: A ConvNet for Motor Imagery Decoding

Incep-EEGNet: A ConvNet for Motor Imagery Decoding

Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

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