Signal Processing and Classification Approaches for Brain-Computer Interface

Al-Ani, Tarik; Trad, Dalila

doi:10.5772/7032

Cited by 29 publications

(18 citation statements)

References 138 publications

(142 reference statements)

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“…The high dimensionality of extracted feature vector adversely affects the classifier performance [17]. Hence feature selection is used to select only relevant features [18]. In this paper, optimization of spatial and spectral filters is done by filter bank common spatial pattern (FBCSP), which is an extension of CSP [19].…”

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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Section: Introductionmentioning

confidence: 99%

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Ghumman,

Singh

2020

IJEAT

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“…Popularity of EEG over other techniques is due to additional advantages it offers such as, inexpensiveness, noninvasiveness, ease of acquisition, high temporal resolution and possibility of implementation in realtime [3]. Major challenges in the field of BCI are, extraction of signal from the brain and it's processing with a high level of precision [4].…”

Section: Introductionmentioning

confidence: 99%

Classification of two mental states using Electroencephalogram signals

Vyas

Mishra

Tiwari

et al. 2013

2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE)

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Electrical activity of human brain changes with the human reactions to the situations, thoughts processing and with different mental states of mind. Brain Computer Interface uses different features of brain electrical activity to create a parallel communication pathway and replaces traditional pathway of nervous system to control numerous applications, by the patients suffering from severe motor disorders. Formation of a Brain Computer Interface is carried out in steps which include preprocessing, feature extraction and classification of Electroencephalogram signals to generate a meaningful command. As Electroencephalogram signals change with different alertness level of human brain, this can cause a false interpretation of Electroencephalogram signals as a patient's alertness may change severely due to medicines with high alcoholic content. A methodology is proposed in present work for feature extraction and classification of Electroencephalogram signals recorded from drowsy and controlled subject. The raw Electroencephalogram data is filtered to extract µ and β wavebands using Butterworth filter. Discrete wavelet coefficients are calculated from filtered data and further processed by Principal Component Analysis for dimensionality reduction. Statistical parameters calculated as features from reduced data set, are used to prepare the input feature vector to train the classifier. Support Vector Machine classifier classifies the two classes of data.

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“…One of the ways to implement these systems is to use motor imageries recorded through Electroencefalogram (EEG) from the cortex, using some kind of processing technique to identify specific patterns related to the intended movement. After that, these patterns can be translated into control commands to external devices (Al-Ani and Trad, 2010;Millan et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…The task of EEG spectral quantification is particularly challenging considering the complexity of the dynamics of non stationary EEG. It is required to take into account the time variation of the relevant frequency components (Liu et al, 2005;Herman et al, 2008;Al-Ani and Trad, 2010;Hema et al, 2010). Another part of the success of a BMI is dependent on subject's training and motivation, making them able to learn to control the intensities of specific frequency bands, which can be used for the communication feature (Herman et al, 2008;Al-Ani and Trad, 2010;Hema et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…It is required to take into account the time variation of the relevant frequency components (Liu et al, 2005;Herman et al, 2008;Al-Ani and Trad, 2010;Hema et al, 2010). Another part of the success of a BMI is dependent on subject's training and motivation, making them able to learn to control the intensities of specific frequency bands, which can be used for the communication feature (Herman et al, 2008;Al-Ani and Trad, 2010;Hema et al, 2010). Herman et al (2008) presented an extensive comparative study involving different approaches to spectral signal representation such as power spectral density techniques, atomic decompositions, timefrequency energy distributions, continuous and discrete wavelet approaches, and also various classifiers aiming at distinguishing between right and left hand motor imageries.…”

Section: Introductionmentioning

confidence: 99%

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A Different Statistical Approach Aiming at EEG Parameter Investigation for Brain Machine Interface Use

Castro

Gerab²

2014

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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A lot of effort has been made to investigate EEG features that could better represent signal characteristics. The results are usually based on the best mean recognition rates and statistical analysis is done only when different methods are compared. In this work, we propose a new approach that applies multiple rate intercomparisons based on large samples aiming at detecting differences among treatments in order to recognize their importance for the classification rates. Ten frequency band compositions expressed by power spectral density averages were extracted from 8 EEG channels during 4 motor imageries, and spatial feature selections were also considered during the recognition process. Classification rate in large samples can be represented by a normal distribution and, for multiple rate inter-comparisons, the level of significance was corrected based on the Bonferroni Method. The variables were considered to be independents and the test was performed as non paired samples in a very conservative approach. The results showed that there are significant differences among cases of spatial feature selection and thus the considered electrodes are important parameters. On the other hand, considering or not the Delta and Theta bands along with different arrangements for Gamma band resulted in no significant difference.

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Signal Processing and Classification Approaches for Brain-Computer Interface

Cited by 29 publications

References 138 publications

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Classification of two mental states using Electroencephalogram signals

A Different Statistical Approach Aiming at EEG Parameter Investigation for Brain Machine Interface Use

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