LDA-based classifiers for a mental tasks-based Brain-Computer Interface

Íáñez, Eduardo; Azorín, José M.; Úbeda, Andrés; Fernández, Eduardo; Sirvent, J.L.

doi:10.1109/icsmc.2010.5642018

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…In those EEG-based classification algorithms, although LDA has a very low computational requirement, it is not suitable for solving nonlinear problems [ 17 ]. NN can make the classification more flexible.…”

Section: Introductionmentioning

confidence: 99%

Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine

Chen

Yan

et al. 2014

Sensors

108

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In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.

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Section: Introductionmentioning

confidence: 99%

Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine

Chen

Yan

et al. 2014

Sensors

108

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“…This may be due to the influence of brain activity by various factors. The use of time domain feature extraction and LDA classifier is very promising for the field of BCI as higher classification accuracies can be achieved in comparison to other methods, [10][11][12]. This will necessarily improve processing time and reduce memory space requirement of the controller used for BCI.…”

Section: Resultsmentioning

confidence: 99%

Time domain Feature extraction and classification of EEG data for Brain Computer Interface

Geethanjali

Mohan

Sen

2012

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery

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In the recent past Brain Computer Interface (BCI) has become popular in the field of rehabilitation engineering for physically challenged people to improve their day-to-day activities independently. A proper BCI can possibly be achieved by proper classification and feature extraction techniques from the Electroencephalogram (EEG) data acquired from the brain. In this paper time domain (TD) features, like Mean Absolute Value (MAV), Zero Crossings (ZC), Slope Sign Changes (SSC) and Waveform Length (WL) is considered for classification of six channels of EEG data with time window of size 1-sec containing 250 data with an overlap of 125 data. A pair-wise combination of five different mental tasks has been considered for classification using Linear Discriminate Analysis (LDA) for seven subjects.Classification accuracies ranging from 67%-100% is obtained for pair-wise classification. The classification accuracy with TD features is found to be considerably increased besides reduction in the memory space and processing time of the classifier used in BCI applications.

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“…Limited classification accuracies were obtained in both studies: 41-86% [33,34]. This may be attributed to insufficiencies in feature mapping by simple linear transformation of the LDA, leading to inefficient construction of the optimal decision function (classification boundary) for multichannel EEG [35,36]. Although ANN-based models offer nonlinear feature mapping abilities during classifications, overfitting often occurs when there are several hyperparameters, e.g., numbers of hidden layers and nodes, to be determined during network optimization [36].…”

Section: Introductionmentioning

confidence: 90%

Evaluation of Post-Stroke Impairment in Fine Tactile Sensation by Electroencephalography (EEG)-Based Machine Learning

Zhang

Huang

et al. 2022

Applied Sciences

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Electroencephalography (EEG)-based measurements of fine tactile sensation produce large amounts of data, with high costs for manual evaluation. In this study, an EEG-based machine-learning (ML) model with support vector machine (SVM) was established to automatically evaluate post-stroke impairments in fine tactile sensation. Stroke survivors (n = 12, stroke group) and unimpaired participants (n = 15, control group) received stimulations with cotton, nylon, and wool fabrics to the different upper limbs of a stroke participant and the dominant side of the control. The average and maximal values of relative spectral power (RSP) of EEG in the stimulations were used as the inputs to the SVM-ML model, which was first optimized for classification accuracies for different limb sides through hyperparameter selection (γ, C) in radial basis function (RBF) kernel and cross-validation during cotton stimulation. Model generalization was investigated by comparing accuracies during stimulations with different fabrics to different limbs. The highest accuracies were achieved with (γ = 21, C = 23) for the RBF kernel (76.8%) and six-fold cross-validation (75.4%), respectively, in the gamma band for cotton stimulation; these were selected as optimal parameters for the SVM-ML model. In model generalization, significant differences in the post-stroke fabric stimulation accuracies were shifted to higher (beta/gamma) bands. The EEG-based SVM-ML model generated results similar to manual evaluation of cortical responses to fabric stimulations; this may aid automatic assessments of post-stroke fine tactile sensations.

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LDA-based classifiers for a mental tasks-based Brain-Computer Interface

Cited by 11 publications

References 16 publications

Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine

Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine

Time domain Feature extraction and classification of EEG data for Brain Computer Interface

Evaluation of Post-Stroke Impairment in Fine Tactile Sensation by Electroencephalography (EEG)-Based Machine Learning

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