Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface

Yu, Zhang; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

doi:10.1016/j.jneumeth.2015.08.004

Cited by 243 publications

(149 citation statements)

References 50 publications

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“…The reasons are as follows: on the one hand, as the feature extraction method, CSP does not take the frequency domain information into account, which will generate unrepresentative ECoG features, and affect classification accuracy later [7]; on the other hand, we apply ELM in feature classification. Due to its random assignment of input weights, it is difficult to get global optimum in the process of finding the optimal weight [8].…”

Section: Results On Extreme Learning Machinementioning

confidence: 99%

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Binary Classification on ECoG Signals Using Optimized Extremely Learning Machine

Zhang¹,

Dai²,

Xu³

et al. 2017

dtcse

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Abstract. In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left hand little finger or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p-value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing non-stationarity of ECoG signal.

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Section: Results On Extreme Learning Machinementioning

confidence: 99%

“…The straight lines parallel to horizontal axis show the mean values corresponding to each trial. Channels are regarded as "good" if they are visibly discriminable on average between two classes [7]. We transfer the features extracted by CSP to SVM, ELM and OELM, results are listed below.…”

Section: Preprocessing Stagementioning

confidence: 99%

Binary Classification on ECoG Signals Using Optimized Extremely Learning Machine

Zhang¹,

Dai²,

Xu³

et al. 2017

dtcse

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“…Out of these potentials, SMR-based BCI provides a high degree of freedom in association with real and imaginary movements of hands, arms, feet and tongue [8]. The neural activities associated with SMRbased motor imagery (MI) BCI are the so-called mu (7)(8)(9)(10)(11)(12)(13) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) rhythms [9]. These rhythms are readily measurable in both healthy and disabled people with neuromuscular injuries.…”

Section: Introductionmentioning

confidence: 99%

“…Few research on feature optimization for MI-based BCI [23] and Steady-State VEP (SSVEP)-based BCI [24] has been carried out recently. In this work, to provide subject-specific optimal features, two different feature selection methodologies, such as minimum Redundancy Maximum Relevancy (mRMR) method and Lasso regularization-based feature selection method are studied.…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach

Sreeja¹,

Samanta²,

Mitra³

et al. 2018

JJCIT

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“…EEG based BCI systems is preferred for other BCI system because of practical use, low cost, good response, portability [3]. There are some most common approaches to EEG based BCI systems that are P300 potentials, event-related (de)synchronization (ERD/ERS), slow cortical potentials, oscillatory activity, and visual evoked potentials (VEPs) [4][5][6][7]. The Steady state visual evoked potential (SSVEP) is a brain response modulated by the frequency of repetitive visual stimulus [8].…”

Section: Introductionmentioning

confidence: 99%

Different Duty Cycle Ratio and Brightness of Visual Stimuli Change to Steady State Visual Evoked Potential Response

Oralhan

Tokmakçı

2016

International Journal of Applied Mathematics, Electronics and Computers

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Stimuli types are very crucial for the performance of electroencephalogram (EEG) based brain computer interface (BCI) systems. This study aims to investigate methods for obtaining higher information transfer rate (ITR) through duty cycle and brightness variation of visual stimuli which have high frequency for steady state visual evoked potential-based BCI. Although previous studies were concentrated on either duty cycle or brightness of stimuli separately, our study focused on the change of duty cycle ratio and brightness of stimuli at the same time. Duty cycle values of 40%, 50%, and 60% were used. During the experiment, 16 flickering stimuli were used on liquid crystal display. Participants gazed to the flicker which had frequency of 15 Hz. Canonical correlation analyses (CCA) was used for channel selection and frequency detection. According to the CCA, the maximum average accuracy of the experiment was 92.54% when the frequency of flicker was in beta band and its duty cycle was 40% with a brightness tuning wave. Under the same conditions stated above, average ITR was improved 16.1% according to the most commonly used flicker model which is square wave and has 50% duty cycle.

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Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface

Cited by 243 publications

References 50 publications

Binary Classification on ECoG Signals Using Optimized Extremely Learning Machine

Binary Classification on ECoG Signals Using Optimized Extremely Learning Machine

Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach

Different Duty Cycle Ratio and Brightness of Visual Stimuli Change to Steady State Visual Evoked Potential Response

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