Idle State Detection in SSVEP-Based Brain-Computer Interfaces

2008 ◽  
Author(s):  
Ran Ren ◽  
Guangyu Bin ◽  
Xiaorong Gao
Keyword(s):  
Brain Computer Interfaces ◽  
Idle State ◽  
Computer Interfaces ◽  
State Detection

An Idle-State Detection Algorithm for SSVEP-Based Brain–Computer Interfaces Using a Maximum Evoked Response Spatial Filter

2015 ◽  
Vol 25 (07) ◽  
pp. 1550030 ◽  
Author(s):  
Dan Zhang ◽  
Bisheng Huang ◽  
Wei Wu ◽  
Siliang Li
Keyword(s):  
Spatial Filter ◽  
Detection Algorithm ◽  
Evoked Response ◽  
Brain Computer Interfaces ◽  
Idle State ◽  
Computer Interfaces ◽  
State Classification ◽  
State Detection ◽  
Binary Classifiers ◽  
Control State

Although accurate recognition of the idle state is essential for the application of brain–computer interfaces (BCIs) in real-world situations, it remains a challenging task due to the variability of the idle state. In this study, a novel algorithm was proposed for the idle state detection in a steady-state visual evoked potential (SSVEP)-based BCI. The proposed algorithm aims to solve the idle state detection problem by constructing a better model of the control states. For feature extraction, a maximum evoked response (MER) spatial filter was developed to extract neurophysiologically plausible SSVEP responses, by finding the combination of multi-channel electroencephalogram (EEG) signals that maximized the evoked responses while suppressing the unrelated background EEGs. The extracted SSVEP responses at the frequencies of both the attended and the unattended stimuli were then used to form feature vectors and a series of binary classifiers for recognition of each control state and the idle state were constructed. EEG data from nine subjects in a three-target SSVEP BCI experiment with a variety of idle state conditions were used to evaluate the proposed algorithm. Compared to the most popular canonical correlation analysis-based algorithm and the conventional power spectrum-based algorithm, the proposed algorithm outperformed them by achieving an offline control state classification accuracy of 88.0 ± 11.1% and idle state false positive rates (FPRs) ranging from 7.4 ± 5.6% to 14.2 ± 10.1%, depending on the specific idle state conditions. Moreover, the online simulation reported BCI performance close to practical use: 22.0 ± 2.9 out of the 24 control commands were correctly recognized and the FPRs achieved as low as approximately 0.5 event/min in the idle state conditions with eye open and 0.05 event/min in the idle state condition with eye closed. These results demonstrate the potential of the proposed algorithm for implementing practical SSVEP BCI systems.

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

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.

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

2018 ◽  
pp. 115-122 ◽  
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. 

scholarly journals Privacy and brain-computer interfaces

2016 ◽  
Vol 46 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Kirsten Wahlstrom ◽  
N. Ben Fairweather ◽  
Helen Ashman
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces
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