A Research Combining Spatial Filter with Autoregressive Model for EEG Feature Extraction

2010 ◽  
Vol 20-23 ◽  
pp. 605-611 ◽  
Author(s):  
Lin Liu ◽  
Qing Guo Wei
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Autoregressive Model ◽  
Information Transfer ◽  
Decisive Role ◽  
Spatial Filter ◽  
Fisher Discriminant Analysis ◽  
Left Hand ◽  
Fisher Discriminant ◽  
Information Transfer Rate

In a noninvasive brain-computer interface (BCI), EEG feature extraction is a key part for improving classification accuracy and resulting information transfer rate, and it has a crucial and decisive role. In this paper, three different methods were proposed that combine spatial filtering with autoregressive model for EEG feature extraction. Six subjects participated in the BCI experiment during which they were asked to imagine movements of left hand and right hand. Each subject carried out four sessions and each session contained 120 trials. EEG data recordings were used for off-line analysis and the 10 leads around C3 and C4 were chosen for feature extraction. Autoregressive model coefficients and the parameters derived from other three methods were proposed as classification features. Fisher discriminant analysis (FDA) was used as linear classifier. The results show that classification accuracy rates obtained from the three proposed methods are far higher than those acquired from autoregressive model coefficients. At the same time the classification results of each subject are very stable, proving the effectiveness of these novel feature methods.

scholarly journals Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Zhihua Huang ◽  
Minghong Li ◽  
Yuanye Ma
Keyword(s):  
Feature Extraction ◽  
Parallel Computing ◽  
Information Transfer ◽  
Small Scale ◽  
Online Experiments ◽  
P300 Speller ◽  
Information Transfer Rate ◽  
Desktop Computers ◽  
The Difference ◽  
Computing Scheme

This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors. To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out. The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. Our method achieved higher accuracies of classification and detection. The ITRs also reflected the superiority of our method. The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. The average feedback time for one round of EEG epochs was 1.57 ms. The proposed method can improve the performance of P300 Speller BCI. Its parallel computing scheme is able to support fast feedback required by online experiments. The number of repeated stimuli can be significantly reduced by our method. The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.

BRAIN SIGNATURES PERSPECTIVE FOR HIGH-SECURITY AUTHENTICATION

2020 ◽  
Vol 32 (04) ◽  
pp. 2050025
Author(s):  
Nikhil Rathi ◽  
Rajesh Singla ◽  
Sheela Tiwari
Keyword(s):  
Classification Accuracy ◽  
Information Transfer ◽  
Rejection Rate ◽  
Internet Banking ◽  
P300 Speller ◽  
Security Authentication ◽  
Information Transfer Rate ◽  
Shoulder Surfing ◽  
Authentication System ◽  
High Level

In the recent past, the web (internet) has emerged as the most interactive authentication system for all of us (i.e. Internet banking passwords, system or building access, and e-payment platforms, etc.) and as a result, traditional authentication systems (like passwords or token-based) are never again more secure i.e. they are vulnerable to attacks. As a result, the security of individual information and safe access to a system winds up prime necessities. Therefore, the EEG-based authentication system has recently become a reasonable key for high-level security. This study centers upon P300 evoked potential-based authentication system designing. In this paper, a new visual stimulus paradigm (i.e. [Formula: see text] P300 speller) using pictures of different objects as stimuli for a person authentication system is designed instead of the conventional character-based paradigm (i.e. [Formula: see text] speller) for increasing the classification accuracy and Information Transfer Rate (ITR). The trial begins by exhibiting a collection of pictures of various objects on four corners of the PC screen comprising of random object pictures (non-target) alongside password pictures (target) that trigger P300 reactions. The P300 reaction’s rightness then checks the identity of the subject concerning the focused pictures (Target). The proposed investigation model achieves higher classification accuracy of 96.78%, along with 0.03075 False Rejection Rate (FRR), 0.03297 False Acceptation Rate (FAR), and ITR of [Formula: see text]. This study has shown that P300-based authentication system has an advantage over conventional methods (Password, Token, etc.) as EEG-based systems cannot be mimicked or forged (like Shoulder surfing in case of password) and can still be used for disabled users with a brain in good running condition. The classification results revealed that the performance of the QDA classifier outperformed other classifiers based on accuracy and ITR.

scholarly journals A Spelling Paradigm With an Added Red Dot Improved the P300 Speller System Performance

2020 ◽  
Vol 14 ◽  
Author(s):  
Yan Wu ◽  
Weiwei Zhou ◽  
Zhaohua Lu ◽  
Qi Li
Keyword(s):  
Classification Accuracy ◽  
Information Transfer ◽  
Event Related Potential ◽  
Virtual Character ◽  
P300 Speller ◽  
Information Transfer Rate ◽  
Control Paradigm ◽  
Character Matrix ◽  
Green Circle ◽  
Visuospatial Information

The traditional P300 speller system uses the flashing row or column spelling paradigm. However, the classification accuracy and information transfer rate of the P300 speller are not adequate for real-world application. To improve the performance of the P300 speller, we devised a new spelling paradigm in which the flashing row or column of a virtual character matrix is covered by a translucent green circle with a red dot in either the upper or lower half (GC-RD spelling paradigm). We compared the event-related potential (ERP) waveforms with a control paradigm (GC spelling paradigm), in which the flashing row or column of a virtual character matrix was covered by a translucent green circle only. Our experimental results showed that the amplitude of P3a at the parietal area and P3b at the frontal–central–parietal areas evoked by the GC-RD paradigm were significantly greater than those induced by the GC paradigm. Higher classification accuracy and information transmission rates were also obtained in the GC-RD system. Our results indicated that the added red dots increased attention and visuospatial information, resulting in an amplitude increase in both P3a and P3b, thereby improving the performance of the P300 speller system.

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