Motor Task Learning in Brain Computer Interfaces using Time-Dependent Regularized Common Spatial Patterns and Residual Networks

As a multichannel spatial filtering technique, common spatial patterns (CSP) have been successfully applied in brain-computer interfaces (BCI) community based on electroencephalogram (EEG). However, it is sensitive to outliers because of the employment of the L2-norm in its formulation. It is beneficial to perform robust modelling for CSP. In this paper, we propose a robust framework, called CSP-Lp/q, by formulating the variances of two EEG classes with Lp- and Lq-norms ( 0 < p and q < 2 ) separately. The method CSP-Lp/q with mixed Lp- and Lq-norms takes the class-wise difference into account in formulating the sample dispersion. We develop an iterative algorithm to optimize the objective function of CSP-Lp/q and show its monotonity theoretically. The superiority of the proposed CSP-Lp/q technique is experimentally demonstrated on three real EEG datasets of BCI competitions.

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Time-Dependent Common Spatial Patterns optimization for EEG signal classification

The First Asian Conference on Pattern Recognition ◽

10.1109/acpr.2011.6166621 ◽

2011 ◽

Author(s):

Tae-Eui Kam ◽

Seong-Whan Lee

Keyword(s):

Spatial Patterns ◽

Time Dependent ◽

Signal Classification ◽

Eeg Signal ◽

Common Spatial Patterns ◽

Eeg Signal Classification

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A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207622 ◽

2020 ◽

pp. 136-142

Author(s):

S. Srilekha ◽

B. Vanathi

Keyword(s):

Infrared Spectroscopy ◽

Healthy Subject ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Economic Conditions ◽

Brain Computer Interfaces ◽

Functional Near Infrared Spectroscopy ◽

Computer Interfaces ◽

Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

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Barriers and solutions for translating brain-computer interfaces for augmentative and alternative communication from research into practice

10.32873/unl.dc.oth.006 ◽

2020 ◽

Author(s):

Kevin Pitt

Keyword(s):

Augmentative And Alternative Communication ◽

Brain Computer Interfaces ◽

Alternative Communication ◽

Computer Interfaces

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Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

Information and Control Systems ◽

10.31799/1684-8853-2018-4-115-122 ◽

2018 ◽

pp. 115-122 ◽

Cited By ~ 1

Author(s):

V. A. Maksimenko ◽

A. A. Harchenko ◽

A. Lüttjohann

Keyword(s):

Epileptic Seizure ◽

Brain Activity ◽

Epileptic Seizures ◽

Automated System ◽

Brain Computer Interfaces ◽

Electrical Brain Activity ◽

Computer Interfaces ◽

Prediction And Prevention ◽

The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans.

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