Search

Highlights • Bag of Features is able the discrimination of imagined speech in electroencephalograms. • Bag of Features aids the transfer learning of imagined speech to recognize new imagined words. • Using time domain features of the electroencephalogram for a Bag of Features model achieves a competitive classification performance. Abstract The Brain–Computer Interfaces (BCI) based on electroencephalograms (EEG) are systems which aim is to provide a communication channel to any person with a computer, initially it was proposed to aid people with disabilities, but actually wider applications have been proposed. These devices allow to send messages or to control devices using the brain signals. There are different neuro-paradigms which evoke brain signals of interest for such purposes. Imagined speech is one of the most recent paradigms, and it is explored in this work, it consists of the internal pronunciation of a word, i.e. a subject imagines the utterance of a word without emitting sounds or articulating facial movements. Under this neuro-paradigm, to increase the initial vocabulary reducing drastically the training time using few or none new data is an open challenge. The proposed method extracts characteristic units (i.e. codewords) of the EEGs associated with the words of an initial vocabulary. Subsequently, a new imagined word is represented with these codewords, and then a classification algorithm is applied. The method was tested both, with and without calibration examples, in a 27 subjects dataset. An initial vocabulary of 4 words, with 33 epochs for each word was considered. The results were obtained by averaging the accuracies of every subject, without calibration data a 65.65% accuracy was achieved. In comparison to the baseline method, which obtained an average accuracy of 68.9%, the proposed method showed no statistical difference. [ABSTRACT FROM AUTHOR]

• This paper proposes a supervised CatPCA approach for performing the imagined speech classification. • The objective function of an optimization problem for designing the unitary matrix is formulated based on both the PCA algorithm and the k means algorithm. • To find the analytical solution of the optimization problem, a property on computing the singular value decomposition (SVD) of a symmetric matrix is employed. • Then, the design of the cluster centers in the k means algorithm is formulated as a quadratic programming. • Finally, the above two procedures are iterated until the algorithm converges. • The required computational power for finding the solution of the optimization problem is low. The dimension of the feature vectors for performing the imagined speech is large. Hence, it is required to reduce their dimension as well as perform the classification simultaneously. This is a categorized principal component analysis (CatPCA) problem. However, the conventional CatPCA methods are unsupervised learning methods and the design of the unitary matrix in the conventional principal component analysis (PCA) algorithms is only based on the PCA approach. Therefore, the classification performance is limited. This paper proposes a supervised CatPCA approach for performing the imagined speech classification. Also, the objective function of an optimization problem for designing the unitary matrix is formulated based on both the PCA algorithm and the k means algorithm. To find the analytical solution of the optimization problem, a property on computing the singular value decomposition (SVD) of a symmetric matrix is employed. Then, the design of the cluster centers in the k means algorithm is formulated as a quadratic programming. Finally, the above two procedures are iterated until the algorithm converges. Since the analytical solutions of these two optimization problems can be derived analytically, the required computational power for finding the solution of the optimization problem is low. To demonstrate the effectiveness of our proposed CatPCA algorithm, the Track 3 of the International brain computer interface (BCI) Competition Database is employed as the dataset for performing the performance evaluation. The computer numerical simulation results show that our proposed CatPCA algorithm yields the higher classification accuracy compared to PCA variants and the dimension reduction related algorithms. Also, the required execution time of our proposed method is lower than those of the states of the arts methods. This demonstrates the effectiveness and the efficiency of our proposed CatPCA algorithm. [ABSTRACT FROM AUTHOR]

• This paper proposes a supervised CatPCA approach for performing the imagined speech classification. • The objective function of an optimization problem for designing the unitary matrix is formulated based on both the PCA algorithm and the k means algorithm. • To find the analytical solution of the optimization problem, a property on computing the singular value decomposition (SVD) of a symmetric matrix is employed. • Then, the design of the cluster centers in the k means algorithm is formulated as a quadratic programming. • Finally, the above two procedures are iterated until the algorithm converges. • The required computational power for finding the solution of the optimization problem is low. The dimension of the feature vectors for performing the imagined speech is large. Hence, it is required to reduce their dimension as well as perform the classification simultaneously. This is a categorized principal component analysis (CatPCA) problem. However, the conventional CatPCA methods are unsupervised learning methods and the design of the unitary matrix in the conventional principal component analysis (PCA) algorithms is only based on the PCA approach. Therefore, the classification performance is limited. This paper proposes a supervised CatPCA approach for performing the imagined speech classification. Also, the objective function of an optimization problem for designing the unitary matrix is formulated based on both the PCA algorithm and the k means algorithm. To find the analytical solution of the optimization problem, a property on computing the singular value decomposition (SVD) of a symmetric matrix is employed. Then, the design of the cluster centers in the k means algorithm is formulated as a quadratic programming. Finally, the above two procedures are iterated until the algorithm converges. Since the analytical solutions of these two optimization problems can be derived analytically, the required computational power for finding the solution of the optimization problem is low. To demonstrate the effectiveness of our proposed CatPCA algorithm, the Track 3 of the International brain computer interface (BCI) Competition Database is employed as the dataset for performing the performance evaluation. The computer numerical simulation results show that our proposed CatPCA algorithm yields the higher classification accuracy compared to PCA variants and the dimension reduction related algorithms. Also, the required execution time of our proposed method is lower than those of the states of the arts methods. This demonstrates the effectiveness and the efficiency of our proposed CatPCA algorithm. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) technology has many applications in various scientific fields, such as used in communication (speech recognition). The data of imagery speech has been collected in electroencephalogram (EEG) signals. In this paper, we propose an approach for EEG feature extraction of imagined speech with high accuracy and efficiency. In this way, we improve the common spatial pattern (CSP) binary algorithm to multiclass level in two parts 'One-vs-One' and 'One-vs-All'. The "Kara One" dataset is used in this research that includes EEG signals of thirteen subjects with twelve trials and sixty-four channels for any four English words signals and seven English phonemes signals. We compared our proposed CSP to other imagined speech feature methods. The classification accuracy of the second part of the proposed method is 97.34% in the subject-wise overall model which is 19.97% better than the best previous result. We have obtained the highest classification accuracy for sixty-four channels, which is the highest accuracy ever achieved using this database. Our proposed model is ready to be tested with more EEG data. This proposed work, which includes an ensemble method for classifying speech imagery words, can greatly contribute to intuitive BCI development using silent speech. [ABSTRACT FROM AUTHOR]

EEG-based Brain–Computer Interfaces (BCI) are an alternative technique that aims to integrate people with severe motor disabilities to their environment. However, they still are not used in everyday life because BCIs are controlled by means of few intuitive electrophysiological sources. To address this problem, work has been carried out with the objective of classifying EEG signals recorded while imagining speech. In this paper, the emerging techniques of sonification and textification are applied to EEG signals, which allows us to characterize EEG signals as either an audio signal or a text document. The aim is to analyze whether these techniques can help to do a better discrimination or to highlight patterns, and assess which of the two methods performs better in order to improve classification results of unspoken words. For proving this we processed signals, from 27 subjects, with and without the application of the proposed methods of sonification and textification. The average accuracy rate using the original EEG signals, the EEG sonified signals and the EEG textified signals are 58.41 ± 12.41%, 63.82 ± 13.44% and 83.34 ± 6.12%, respectively. It can be clearly noticed that the methods of sonification and textification of EEG improve the classification rates obtained using the EEG signals in their original form. [ABSTRACT FROM AUTHOR]

Brain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods. An issue with incremental learning methods is degradation of the decoding capacity of the original model when new classes are added. In this study, a class-incremental neural network method is proposed to increase the vocabulary of imagined speech. The results indicate a stable model that did not degenerate when a new word was integrated. The proposed method allows for the inclusion of newly imagined words without a significant loss of total accuracy for the two datasets. • Model based on neural networks for imagined speech discrimination. • Intra-subject incremental learning approach of imagined speech BCIs. • Incremental twin neural networks for incremental learning. [ABSTRACT FROM AUTHOR]

An asynchronous Brain–Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or to emit a message at the moment the user desires to by decoding EEG signals of imagined speech. In order to correctly implement these types of BCI, we must be able to detect from a continuous signal, when the subject starts to imagine words. In this work, five methods of feature extraction based on wavelet decomposition, empirical mode decomposition, frequency energies, fractal dimension and chaos theory features are presented to solve the task of detecting imagined words segments from continuous EEG signals as a preliminary study for a latter implementation of an asynchronous BCI based on imagined speech. These methods are tested in three datasets using four different classifiers and the higher F 1 s c o r e s obtained are 0.73, 0.79, and 0.68 for each dataset, respectively. This results are promising to build a system that automatizes the segmentation of imagined words segments for latter classification. • A new method for detecting imagined words segments in EEG signals is described. • Feature sets based on DWT, EMD, frequency energies, and chaos theory were evaluated. • The experimental scheme for detecting imagined word segments is subject-specific. • The system is being designed to be a module of a BCI based on imagined words. [ABSTRACT FROM AUTHOR]

• This study addresses the problem of EEG classification during imagined speech for BCI development. • We propose a new Riemannian distance for correntropy spectral density matrices in multichannel EEG signals. • We evaluated the proposed approach over a dataset of imagined speech, EEG signals from 8 subjects. • The average accuracy of classification for imagined speech pairs in the proposed method shows superiority over other methods for imagined speech classification. Several current brain–computer interface (BCI) systems are based on imagined speech. This means that these systems are controlled only by thinking about a speech without verbally expressing it. Imagined speech recognition using electroencephalogram (EEG) signals is much more convenient than other methods such as electrocorticogram (ECoG), due to its easy, non-invasive recording. So, we proposed an approach for EEG classification of imagined speech with high accuracy and efficiency. In this work, correntropy spectral density (CSD) matrices are evaluated for EEG signals obtained from different channels, and the distances between these matrices are considered as measures for imagined speech recognition. Riemannian distance benefits from simplicity and accuracy and it has achieved high scores in BCI competitions. Also, in this work, channel selection and frequency band detection during imagined speech is evaluated with statistical methods. The "Kara One" database is used in this research that includes EEG signals of eight subjects during imagined speech of four English words. We evaluated the proposed approach in comparison with the results of other imagined speech classification methods. Average classification accuracy of the proposed method is 90.25% during imagined speech for all subjects in KARA One database. The simulation results of this paper show the efficiency and accuracy of Riemannian distance of CSD and the superiority of the proposed method over other methods for imagined speech classification. [ABSTRACT FROM AUTHOR]