Channels selection for motor imagery paradigm — An Itakura distance based method

Eva, Oana-Diana; Lazar, Anca Mihaela

doi:10.1109/ehb.2015.7391469

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…In [20], where Itakura distance based method is used and 20 subjects from the first database, the classification rates are approximately the same with the actual results. For example, subject RR28I achieved 81.67%, 86.67%, 85.00% classification rates using LDA, QDA and MD classifiers, compared to 74.09%, 79.68%, 79.56% for PLI and 56.20%, 55.60% and 55.84% for WPLI.…”

Section: Classifierssupporting

confidence: 50%

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Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

Eva¹

2016

ijacsa

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Section: Classifierssupporting

confidence: 50%

“…While performing a mental activity as left/right hand movement or imagination changes appear in the sensorimotor area in the corresponding signal power of Mu (8)(9)(10)(11)(12) and Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) rhythms.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

Eva¹

2016

ijacsa

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“…Figure 15 presents the summary of all classifiers used in this paper. The implementation details of the classifier used in this study are mentioned for SVM [ 1 , 3 , 42 , 46 , 51 , 54 , 56 , 59 , 60 , 62 , 70 , 76 , 79 ], LDA [ 26 , 52 , 55 , 58 , 76 , 77 , 78 , 79 ], CNN [ 21 , 24 , 27 , 32 ], Fisher’s LDA [ 53 , 80 ], FDA [ 23 , 43 ], and other’s classifier [ 22 , 25 , 43 , 47 , 57 , 61 , 64 , 73 , 74 , 79 ].…”

Section: Discussion and Guidelinesmentioning

confidence: 99%

“…The changes in mental tasks (imagination of the left and the right) vs. relaxation times are measured by Itakura distance (I.D.) [ 74 ]. The EEG data were acquired in conjunction with the BCI2000 platform utilizing the g.MOBIlab+ module from G.Tech Guger Technologies [ 75 ].…”

Section: Motor Imagery Eeg Classification For Channel Selectionmentioning

confidence: 99%

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives

2022

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Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain’s motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10–30% of total channels, provided excellent performance compared to other existing studies.

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“…Phase locking value (PLV) [22], phase lag index (PLI) [23] and weighted phase lag index (wPLI) [24] are used to measure the synchronization between two signals ( ) and ( ).…”

Section: Methodsmentioning

confidence: 99%

Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

Eva¹,

Lazar²

2017

ijacsa

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Abstract-A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.

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Channels selection for motor imagery paradigm — An Itakura distance based method

Cited by 3 publications

References 12 publications

Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives

Feature Extraction and Classification Methods for a Motor Task Brain Computer Interface: A Comparative Evaluation for Two Databases

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