Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification

Kam, Tae-Eui; Suk, Heung-Il; Lee, Seong–Whan

doi:10.1016/j.neucom.2012.12.002

Cited by 108 publications

(46 citation statements)

References 29 publications

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“…It is clear that there's no specified filter algorithm or specified filter parameters that provides always better accuracy for any subject. This confirms the variability of frequency domain due the intrinsic characteristic of subject's EEG signals [11]. Moreover, in some cases we noted that filtering deteriorated dramatically the classification accuracy relative to non-using of filter: for example for subject number B3 of data set IIIa, using Kaiserwin filter with SNR of 70 dB decreases the performance of the system by more than 50%.…”

Section: Preliminary Observations and Discussionsupporting

confidence: 77%

“…For this range of frequencies, these data should be processed through one of the above mentioned filter techniques to keep the interesting frequency of the system in its bandwidth. In our case, the theoretical frequency response for the MI area is located on the µ-rhythm [8][9][10][11][12][13] Hz and β-rhythm [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz [2]. In our case, the only known parameter is the attenuation band which should be close to zero decibel to avoid any distortion of the EEG information [16].…”

Section: Eeg Filter Designmentioning

confidence: 99%

“…A lot of researchers in the literature, mentioned the uses of µ and β bands for motor imagery applications and demonstrate that the most responsive frequency bands vary according to each subject [11] [10] [20]. These two major points are totally different and each one should be treated alone.…”

Section: Figure 4: Minimum Filter Order For Mi-eeg Datamentioning

confidence: 99%

See 2 more Smart Citations

An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

Belwafi¹,

Djemal²,

Ghaffari³

et al. 2014

2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

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Section: Preliminary Observations and Discussionsupporting

confidence: 77%

Section: Eeg Filter Designmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

Belwafi¹,

Djemal²,

Ghaffari³

et al. 2014

2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

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“…One popular paradigm involves instructing subjects to imagine right and left hand movements. Differential activation of brain regions associated with the motor control of these respective body parts can then be decoded from EEG signals [4], [5]. Motor imagery (MI) can be extended to a vast array of applications.…”

Section: Introductionmentioning

confidence: 99%

Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements From Electroencephalogram Signals

Kim

Bießmann

Lee

2015

IEEE Trans. Neural Syst. Rehabil. Eng.

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130

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Decoding motor commands from noninvasively measured neural signals has become important in brain-computer interface (BCI) research. Applications of BCI include neurorehabilitation after stroke and control of limb prostheses. Until now, most studies have tested simple movement trajectories in two dimensions by using constant velocity profiles. However, most real-world scenarios require much more complex movement trajectories and velocity profiles. In this study, we decoded motor commands in three dimensions from electroencephalography (EEG) recordings while the subjects either executed or observed/imagined complex upper limb movement trajectories. We compared the accuracy of simple linear methods and nonlinear methods. In line with previous studies our results showed that linear decoders are an efficient and robust method for decoding motor commands. However, while we took the same precautions as previous studies to suppress eye-movement related EEG contamination, we found that subtracting residual electro-oculogram activity from the EEG data resulted in substantially lower motor decoding accuracy for linear decoders. This effect severely limits the transfer of previous results to practical applications in which neural activation is targeted. We observed that nonlinear methods showed no such drop in decoding performance. Our results demonstrate that eye-movement related contamination of brain signals constitutes a severe problem for decoding motor signals from EEG data. These results are important for developing accurate decoders of motor signal from neural signals for use with BCI-based neural prostheses and neurorehabilitation in real-world scenarios.

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“…The performance of SLDA is evaluated on the BCI Competition IV dataset 2a [31]. The results are compared to the winner of the competition in this dataset [8], as well as other methods previously tested on this dataset [6,9,[32][33][34][35][36].…”

mentioning

confidence: 99%

Ensemble learning for classification of motor imagery tasks in multiclass brain computer interfaces

Nicolás-Alonso

Corralejo

Gómez‐Pilar

et al. 2014

2014 6th Computer Science and Electronic Engineering Conference (CEEC)

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The difficulty to decode brain signals in a reliable way limits practical motor imagery-based brain computer interface (MI-BCI) applications. The aim of this paper is to propose a classification framework that handle spectral, temporal, and spatial characteristics associated with execution of motor imagery tasks, as well as the temporal variability in EEG data. An ensemble learning approach such as stacked generalization is used to combine information coming from multiple sources. The session-to-session performance of the proposed classifier ensemble is evaluated on a multiclass problem posed in the BCI Competition IV dataset 2a. The results yields a higher mean kappa of 0.66 compared to 0.62 from the baseline linear discriminant analysis (LDA). Also, our approach outperforms the winner of the BCI Competition IV dataset 2a and other studies reported in BCI literature.

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Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification

Cited by 108 publications

References 29 publications

An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements From Electroencephalogram Signals

Ensemble learning for classification of motor imagery tasks in multiclass brain computer interfaces

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