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[PDF] Top 20 Classification of EEG-based motor imagery BCI by using ECOC

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Classification of EEG-based motor imagery BCI by using ECOC

Classification of EEG-based motor imagery BCI by using ECOC

... The EEG signal is usually contaminated with artifacts, which are either easily removed by an appropriate filtering ...by using source decomposition techniques such as independent component analysis (ICA) ... See full document

11

EEG feature comparison and classification of simple and compound limb motor imagery

EEG feature comparison and classification of simple and compound limb motor imagery

... in BCI systems using the optimal discriminant hyperplane to identify classes, which is adopted here for classification of seven kinds of MI pat- ...multi-class classification problem. The ... See full document

12

A novel channel selection method for optimal classification in different motor imagery BCI paradigms

A novel channel selection method for optimal classification in different motor imagery BCI paradigms

... rhythm based BCI system utilizing MI, the ability to effectively clas- sify distinct patterns of MI is ...method based on IterRelCen per- formed excellently in classification and optimal ... See full document

18

Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects

Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects

... The EEG data was classified using the least square (LS) linear classifier in case of the naive subjects through the common spatial pattern (CSP) ...accuracy using the training data set and test data ... See full document

12

EEG oscillatory patterns and classification of sequential compound limb motor imagery

EEG oscillatory patterns and classification of sequential compound limb motor imagery

... two classification methods, we also computed and compared the classification accuracies within the same frequency ...mean classification accuracy is ...Hz using the multi-CSP-based ... See full document

12

Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification

Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification

... variability classification [12], signal to noise enhancement [13] and seizure prediction ...MI EEG data as all the channels on the head do not provide independent information and there are high correlations ... See full document

29

Data space adaptation for multiclass motor imagery-based BCI

Data space adaptation for multiclass motor imagery-based BCI

... these BCI is the publicly available data set, BCI Competition IV dataset 2a ...contains EEG data from nine users who each completed two sessions, each containing six runs, on different ...of ... See full document

5

EEG-based brain-computer interfaces using motor-imagery : techniques and challenges

EEG-based brain-computer interfaces using motor-imagery : techniques and challenges

... of BCI technologies has emerged, particularly in the areas of entertainment, gaming and affective computing ...the BCI and the user, particularly the influence on a user’s mood, can raise ethical issues, ... See full document

34

Detection of motor imagery EEG signals employing Naïve Bayes based learning process

Detection of motor imagery EEG signals employing Naïve Bayes based learning process

... detect EEG signals of MI activities for the application of ...how EEG signals are organised to detect different categories of MI ...allocation based algorithm to determine representatives sample ... See full document

21

Electroencephalography-based endogenous brain–computer interface for online communication with a completely locked-in patient

Electroencephalography-based endogenous brain–computer interface for online communication with a completely locked-in patient

... implement BCI systems with the ultimate goal of communi- cating with patients in ...decades, EEG-based BCI systems have been developed with elaborately designed paradigms [7–12], with greatly ... See full document

13

33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface

33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface

... https://doi.org/10.4236/jbise.2017.106025 329 J. Biomedical Science and Engineering yield a linear phase response. Moreover, to remove this linear phase response and achieve a zero-phase filtering, the forward backward ... See full document

16

Connectivity analysis from EEG phase synchronisation in emotional BCI

Connectivity analysis from EEG phase synchronisation in emotional BCI

... Li, Improving the separability of motor imagery eeg signals using a ross orrelation-based least square support ve tor ma hine for brain- omputer interfa e, IEEE Transa tions on Neural Sy[r] ... See full document

207

EEG Classification in Brain Computer Interface (BCI):  A Pragmatic Appraisal

EEG Classification in Brain Computer Interface (BCI): A Pragmatic Appraisal

... a BCI based on electrocorticographic (ECoG) was worked upon to enable users control a one-dimensional computer cursor rapidly and accurately and finally suggested that an ECoG-based BCI could ... See full document

11

Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non invasive BCI

Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non invasive BCI

... Spatial filtering method works on forming the filter mask made from the most influential electrodes for the MI signals. This mask, when applied on the test signal, provides us with the time series having an optimum ... See full document

6

Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface

Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface

... the motor imagery thought process from brain using Electro-encephalogram (EEG) and process the data using signal processing techniques to classify the motor imagery ... See full document

5

Wavelet Based Classification of Finger Movements 
Using EEG Signals

Wavelet Based Classification of Finger Movements Using EEG Signals

... Society. EEG signals contain a set of signals which are classified according to their ...the motor activities of the ...in EEG based BCI systems because the artifact signals are likely ... See full document

8

Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power

Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power

... was based on the duration of the effects with another cephalic montage with a current density that was similar to the lowest current density that was applied ...decontaminating EEG samples that have noise ... See full document

16

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG based BCI systems

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG based BCI systems

... Core measures from graph theory referring to the concepts of brain integration and segregation have been employed to determine the brain connectivity under different situations [13]. Networks can be characterised at ... See full document

6

Multiclass EEG motor imagery classification with sub band common spatial patterns

Multiclass EEG motor imagery classification with sub band common spatial patterns

... multiclass EEG signals of same limb ...multiclass EEG signal acquired from the same ...limb EEG data for feature ...for classification on the basis of extracted fea- ... See full document

9

Classification of Motor Imagery Based EEG Signals

Classification of Motor Imagery Based EEG Signals

... devices. BCI acknowledges the target of the user with electrophysiological and alternative signals from the brain, decrypts in progress neural activity and changes over it into yield directions to satisfy the user ... See full document

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