Classification of EEG Motor Imagery Multi Class Signals Based on Cross Correlation

Krishna, D. Hari; Pasha, I. A.; Savithri, T. Satya

doi:10.1016/j.procs.2016.05.198

Cited by 32 publications

(11 citation statements)

References 9 publications

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“…It is widely known phenomenon that EEG rhythmic activities occurs over motor related areas of brain while performing or imaging any movement related work [12,21]. Different spatio-temporal pattern of EEG can be predicted depending on the type of imagery performed as imagining motor actions can moderate the motor related rhythm and result in power changes [22] [23]. Over the past years a lot of work has been done in the field of motor imagery based development of BCI devices.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Action Oriented Effects on Perceptual Process of Object Recognition Using Physiological Responses

Sharma¹,

Mishra²,

Kumar

et al. 2018

Intelligent Human Computer Interaction

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Section: Introductionmentioning

confidence: 99%

Analysis of Action Oriented Effects on Perceptual Process of Object Recognition Using Physiological Responses

Sharma¹,

Mishra²,

Kumar

et al. 2018

Intelligent Human Computer Interaction

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“…We compare the classification accuracy of the classifier built using the proposed cross-correlation features and the classifier built using other types of features, viz. statistical [39] and Hjorth [32] extracted from the crosscorrelation sequence by other researchers. Figures 12 and 13 for the channels 1 and 2 show that the proposed features are more discriminative than other types of features.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, a reference signal is different for each channel. Bose et al [32] and Krishna et al [39] extracted different types of features, viz. Hjorth parameters, statistical features, etc.…”

Section: ) Step 2: Cross-correlation Feature Extractionmentioning

confidence: 99%

Cross-Correlation Aided Ensemble of Classifiers for BCI Oriented EEG Study

et al. 2019

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Recently, Brain-computer interface (BCI) oriented electroencephalographic (EEG) studies have received due attention for decoding human brain signals corresponding to a specific mental state and providing an alternate solution to the disabled or paralyzed persons for communicating with the computer, robotic arm, or various neural prostheses. In this paper, we propose a two-phase approach to distinguish EEG signals of different mental tasks. The first phase combines the cross-correlation features and slow cortical potentials mean extracted from the most significant channels to form feature vectors. The second phase performs a classification of these feature vectors using SVM and KNN classifiers. It further boosts the classification performance by creating an ensemble of SVM classifiers trained with complementary feature sets extracted during the first phase. EEG signals generated for the same mental task are similar in shape to each other and dissimilar to other activities. The basic principle of cross-correlation is to measure the similarity in shape between two signals which makes it suitable for the EEG analysis. We test the performance of the proposed approach on the BCI competition II dataset Ia representing the cursor movement EEG data for a healthy subject. Experimental results on this dataset demonstrate a significant improvement in the classification accuracy compared to other reported results. Moreover, the proposed work requires fewer channels and features compared to the recent study, which uses all six channels and 42 features, manifesting the efficacy of the proposed work.INDEX TERMS Brain-computer interface (BCI), cross-correlation, EEG classification, ensemble.

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“…The most used signal acquisition technique in BCI studies is the electroencephalography (EEG) due to its simplicity and usability [3]. EEG provides high temporal resolution at low cost, making it popular among researchers [4]. In EEG based BCI systems, noninvasive sensors are placed on the scalp of user to sense electrical activity of the brain [5].…”

Section: Introductionmentioning

confidence: 99%

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Ghumman,

Singh

2020

IJEAT

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Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

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Classification of EEG Motor Imagery Multi Class Signals Based on Cross Correlation

Cited by 32 publications

References 9 publications

Analysis of Action Oriented Effects on Perceptual Process of Object Recognition Using Physiological Responses

Analysis of Action Oriented Effects on Perceptual Process of Object Recognition Using Physiological Responses

Cross-Correlation Aided Ensemble of Classifiers for BCI Oriented EEG Study

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

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