Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces

Wei, Qingguo; Wei, Zhonghai

doi:10.3233/bme-151451

Cited by 22 publications

(19 citation statements)

References 16 publications

(15 reference statements)

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“…Two separate experiments were carried out to evaluate the performance of the proposed system. In the first experiment, we selected a subset of 25 channels (as in [49]) that are instrumental for the neurophysiological discrimination between the tasks.…”

Section: Experimental Results Analysismentioning

confidence: 99%

“…• BPSO-CSP: Ten bandpass filters having bandwidth of 4 Hz in the range of 8 Hz to 30 Hz with an overlap of 2 Hz have been used. Only 25 selected channels of data were used for processing as in Wei and Wei [49].…”

Section: Methodsmentioning

confidence: 99%

“…To address the issue of computation cost due to the use of large number of sub-bands and due to the fact that some sub-bands may contain redundant information, a sub-band binary particle swarm optimization approach (BPSO) using CSP [49] has been proposed. In sub-band BPSO-CSP, the EEG signals are decomposed into subbands using multiple filter banks as in DFBCSP.…”

Section: Introductionmentioning

confidence: 99%

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A new parameter tuning approach for enhanced motor imagery EEG signal classification

Kumar

Sharma

2018

Med Biol Eng Comput

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A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals. Graphical abstract ᅟ.

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Section: Experimental Results Analysismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new parameter tuning approach for enhanced motor imagery EEG signal classification

Kumar

Sharma

2018

Med Biol Eng Comput

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“…This would provide signals that are as discriminative as possible between different tasks thereby boosting the ability to correctly recognize different categories of MI tasks. Filter bank common spatial pattern (FBCSP) [28], discriminative FBCSP (DFBCSP) [29], and binary particle swarm optimization (BPSO) for frequency band selection [30] are some of the methods proposed to tackle this problem. In the FBCSP approach [28], the raw EEG signal is filtered using multiple zero-phase Chebyshev Type II Infinite Impulse Response (IIR) filter banks in the range of 4-40 Hz, each having a bandwidth of 4 Hz.…”

Section: Introductionmentioning

confidence: 99%

“…The DFBCSP method outperformed the FBCSP method. Wei and Wei [30] proposed using BPSO for selecting the best frequency sub-bands from ten frequency sub-bands in the range of 8-30 Hz each having bandwidth of 4 Hz with an overlap of 2 Hz. Due to computational complexity, the authors performed evaluation using selected 24 and 14 channels.…”

Section: Introductionmentioning

confidence: 99%

PRICAI 2019: Trends in Artificial Intelligence

Zhang¹,

Orgun²

2019

Lecture Notes in Computer Science

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Over the last decade, processing of biomedical signals using machine learning algorithms has gained widespread attention. Amongst these, one of the most important signals is electroencephalography (EEG) signal that is used to monitor the brain activities. Brain-computer-interface (BCI) has also become a hot topic of research where EEG signals are usually acquired using non-invasive sensors. In this work, we propose a scheme based on common spatial spectral pattern (CSSP) and optimization of temporal filters for improved motor imagery (MI) EEG signal recognition. CSSP is proposed as it improves the spatial resolution while the temporal filter is optimized for each subject as the frequency band which contains most significant information varies amongst different subjects. The proposed scheme is evaluated using two publicly available datasets: BCI competition III dataset IVa and BCI competition IV dataset 1. The proposed scheme obtained promising results and outperformed other state-of-the-art methods. The findings of this work will be beneficial for developing improved BCI systems.

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Deep Learning in Classifying Depth of Anesthesia (DoA)

AlMeer

Abbod

2018

Advances in Intelligent Systems and Computing

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Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces

Cited by 22 publications

References 16 publications

A new parameter tuning approach for enhanced motor imagery EEG signal classification

A new parameter tuning approach for enhanced motor imagery EEG signal classification

PRICAI 2019: Trends in Artificial Intelligence

Deep Learning in Classifying Depth of Anesthesia (DoA)

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