scholarly journals Asynchronous Control of P300-Based Brain–Computer Interfaces Using Sample Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 230 ◽  
Author(s):  
Víctor Martínez-Cagigal ◽  
Eduardo Santamaría-Vázquez ◽  
Roberto Hornero
Keyword(s):  
Sample Entropy ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Asynchronous Control ◽  
Computer Interfaces ◽  
Average Accuracy ◽  
Optimization Stage ◽  
Common Combination ◽  
Visual Oddball ◽  
Bci System

Brain–computer interfaces (BCI) have traditionally worked using synchronous paradigms. In recent years, much effort has been put into reaching asynchronous management, providing users with the ability to decide when a command should be selected. However, to the best of our knowledge, entropy metrics have not yet been explored. The present study has a twofold purpose: (i) to characterize both control and non-control states by examining the regularity of electroencephalography (EEG) signals; and (ii) to assess the efficacy of a scaled version of the sample entropy algorithm to provide asynchronous control for BCI systems. Ten healthy subjects participated in the study, who were asked to spell words through a visual oddball-based paradigm, attending (i.e., control) and ignoring (i.e., non-control) the stimuli. An optimization stage was performed for determining a common combination of hyperparameters for all subjects. Afterwards, these values were used to discern between both states using a linear classifier. Results show that control signals are more complex and irregular than non-control ones, reaching an average accuracy of 94.40 % in classification. In conclusion, the present study demonstrates that the proposed framework is useful in monitoring the attention of a user, and granting the asynchrony of the BCI system.

scholarly journals Correction: Martínez-Cagigal, V.; Santamaría-Vázquez, E.; Hornero, R. Asynchronous Control of P300-Based Brain–Computer Interfaces Using Sample Entropy. Entropy 2019, 21, 230

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 505
Author(s):  
Víctor Martínez-Cagigal ◽  
Eduardo Santamaría-Vázquez ◽  
Roberto Hornero
Keyword(s):  
Sample Entropy ◽  
Brain Computer Interfaces ◽  
Asynchronous Control ◽  
Computer Interfaces

Figure 5 of the original paper contains errors [...]

Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces

2020 ◽  
pp. 2150003
Author(s):  
Pasquale Arpaia ◽  
Francesco Donnarumma ◽  
Antonio Esposito ◽  
Marco Parvis
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Binary Classification ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Selection For ◽  
A Performance

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77–83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.

scholarly journals Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171431-171451 ◽  
Author(s):  
Muhammad Tariq Sadiq ◽  
Xiaojun Yu ◽  
Zhaohui Yuan ◽  
Fan Zeming ◽  
Ateeq Ur Rehman ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Empirical Wavelet Transform

Classification of EEG Signals Based on Filter Bank and Sparse Representation in Motor Imagery Brain-Computer Interfaces

2019 ◽  
Vol 29 (03) ◽  
pp. 2050034 ◽  
Author(s):  
Jin Wang ◽  
Qingguo Wei
Keyword(s):  
Sparse Representation ◽  
Motor Imagery ◽  
Filter Bank ◽  
Classification Performance ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Fisher Score ◽  
Common Spatial Pattern ◽  
Signal Processing Algorithm ◽  
Computer Interfaces

To improve the classification performance of motor imagery (MI) based brain-computer interfaces (BCIs), a new signal processing algorithm for classifying electroencephalogram (EEG) signals by combining filter bank and sparse representation is proposed. The broadband EEG signals of 8–30[Formula: see text]Hz are segmented into 10 sub-band signals using a filter bank. EEG signals in each sub-band are spatially filtered by common spatial pattern (CSP). Fisher score combined with grid search is used for selecting the optimal sub-band, the band power of which is employed for designing a dictionary matrix. A testing signal can be sparsely represented as a linear combination of some columns of the dictionary. The sparse coefficients are estimated by [Formula: see text] norm optimization, and the residuals of sparse coefficients are exploited for classification. The proposed classification algorithm was applied to two BCI datasets and compared with two traditional broadband CSP-based algorithms. The results showed that the proposed algorithm provided superior classification accuracies, which were better than those yielded by traditional algorithms, verifying the efficacy of the present algorithm.

Detection of the Intention of Direction Changes During Gait Through EEG Signals

2021 ◽  
pp. 2150015
Author(s):  
Paula Soriano-Segura ◽  
Eduardo Iáñez ◽  
Mario Ortiz ◽  
Vicente Quiles ◽  
José M. Azorín
Keyword(s):  
Neural Plasticity ◽  
Eeg Signals ◽  
Rehabilitation Process ◽  
Computer Interfaces ◽  
Temporal Features ◽  
Average Accuracy ◽  
Technological Tool ◽  
Handicapped People ◽  
Intention To Change ◽  
Selection Of

Brain–Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason, current BCIs have to be improved to allow an accurate and natural use of external devices. This work proposes a novel methodology for the detection of the intention to change the direction during gait based on event-related desynchronization (ERD). Frequency and temporal features of the electroencephalographic (EEG) signals are characterized. Then, a selection of the most influential features and electrodes to differentiate the direction change intention from the walking is carried out. Best results are obtained when combining frequency and temporal features with an average accuracy of [Formula: see text]%, which are promising to be applied for future BCIs.

Non-Invasive Brain–Computer Interfaces: Development and Rehabilitation for Motor Disability

2021 ◽  
Vol 65 (1) ◽  
pp. 1264-1268
Author(s):  
Ayushi Das ◽  
Dan Nathan-Roberts
Keyword(s):  
Motor Impairment ◽  
The United States ◽  
Brain Computer Interfaces ◽  
Motor Disability ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Communication Disabilities ◽  
The Face ◽  
Disability Research ◽  
Bci System

Around 61 million people in the United States suffer from different forms of disability; of these, 13.7% suffer from a motor disability. Research in brain–computer interfaces (BCIs) has focused on curbing communication disabilities due to motor impairment. Many challenges and future developments lie ahead in the BCI world. The research on paradigms that guide the way to make communication easy for a person with motor impairment is the pillar of the BCI system. The purpose of this paper is to synthesize the developments in non-invasive brain–computer interface and evaluate them. The authors discuss components of the BCI system and how it is formed. Neurofeedback based on different modalities is also analyzed. The results from initial studies have been successful, but paradigms and neurofeedback technologies have immense development potential, which can change the face of BCI systems for rehabilitation of motor disability.

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