Adapting subject specific motor imagery EEG patterns in space–time–frequency for a brain computer interface

Ince, Nuri F.; Goksu, Fikri; Tewfik, A.H.; Arıca, Sami

doi:10.1016/j.bspc.2009.03.005

Cited by 76 publications

(33 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Methods such as [33][34][35][36] have used these classi ers in BCI systems. LDA is suitable for online BCI systems because of a low computational cost, which persuades researchers to use it in motor imagery-based BCI systems.…”

Section: Classi Cation: Linear Classi Ers Such As Linearmentioning

confidence: 99%

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

et al. 2018

View full text Add to dashboard Cite

Abstract. Brain Computer Interface (BCI) systems, which are based on motor imagery, enable humans to command arti cial peripherals by merely thinking about the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAs) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It also uses Separable Common Spatio Spectral Pattern (SCSSP) method in order to extract features. Simulation results prove achieved performances of 73.54% for BCI competition III-dataset V, 67.2% for BCI competition IV-dataset 2a with all four classes, 80.55% for BCI competition IV-dataset 2a with the rst two classes, and 81.9% for captured signals. Moreover, the nal reported hardware resources determine its e ciency as a result of using retiming and folding techniques from the VLSI architecture' perspective. The complete proposed BCI system achieves not only excellent recognition accuracy, but also remarkable implementation e ciency in terms of portability, power, time, and cost.

show abstract

Section: Classi Cation: Linear Classi Ers Such As Linearmentioning

confidence: 99%

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The first of these paradigms relies on the ability of the operator in modifying -by imagining the process of moving parts of both sides of his/her body (e.g. opening or closing the right or the left hand) -the activity of the motor cortex [5], while the second makes use of a specific event-related potential, the P300 wave, to characterize the interaction between the operator and a command interface [6]. Finally, the SSVEP paradigm, the subject of this study, is based on the analysis of oscillating EEG patterns that are generated in the cortex in response to certain visual stimuli.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs

Carvalho

Costa

Uribe

et al. 2015

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

“…Second, the performance of CSP severely depends on the preprocessing including the section of frequency band and time windows [15], [17]. Only having the EEG signals bandpass filtered through the frequency domain of interest, high or low signal variances could reflect a strong or weak rhythmic activity respectively [18]; When ERD does not occur, the CSP can not work well.…”

Section: Introductionmentioning

confidence: 99%

Extracting nonlinear correlation for the classification of single-trial EEG in a finger movement task

Xie

Tang

2014

2014 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

The famous common spatial patterns (CSP) algorithm has shown to be useful for event-related desynchronization (ERD) feature extraction of multi-channel electroencephalogram (EEG) signals. Actually, CSP only extracts the linear correlation between each pair of channels. The performance of CSP severely depends on the preprocessing. Moreover, CSP and the subsequent classifier are not optimized by the same criteria. In this paper, we investigated the nonlinear correlation between channels with kernel technique, and proposed a unified prediction framework based on linear ridge regression model. This prediction framework integrates preprocessing, feature extraction and classification, can automatically select the time windows, frequency bands and regularization parameter by minimizing leave-one-out crossvalidation error through gradient descent. Experimental results on the dataset IV, BCI competition II show the effectiveness of our approach.

show abstract

Adapting subject specific motor imagery EEG patterns in space–time–frequency for a brain computer interface

Cited by 76 publications

References 17 publications

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs

Extracting nonlinear correlation for the classification of single-trial EEG in a finger movement task

Contact Info

Product

Resources

About