Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

doi:10.3390/s17112576

Cited by 66 publications

(36 citation statements)

References 23 publications

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“…The increasing awareness of brain–computer interfaces (BCI) for brain signal analysis has sparked new interest in electroencephalogram (EEG) acquisition device development. Various rehabilitation [ 1 ], entertainment, and even security [ 2 ] applications can be implemented by post-processing [ 3 , 4 , 5 ] such electrical signals recorded from the human scalp. However, developing a BCI is a challenging task due to the noisy and variable nature of the EEG signal itself.…”

Section: Introductionmentioning

confidence: 99%

Development of a Modular Board for EEG Signal Acquisition

Uktveris

Jusas

2018

Sensors

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The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0–70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.

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Section: Introductionmentioning

confidence: 99%

Development of a Modular Board for EEG Signal Acquisition

Uktveris

Jusas

2018

Sensors

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“…The efficiency of preprocessing Figure 2. Timing scheme of the paradigm for data set 2a from BCI Competition IV [33] phase has a direct impact on efficiency attainment of overall BCI system [34]. The raw data set stored in GDF format was loaded by using functions of BioSig toolbox [35].…”

Section: Preprocessingmentioning

confidence: 99%

“…ICA is employed to remove 3 EOG channels corresponding to eye movement [38], and remaining 22 EEG channels were used for further processing. phase has a direct impact on efficiency attainment of overall BCI system [34]. The raw data set stored in GDF format was loaded by using functions of BioSig toolbox [35].…”

Section: Preprocessingmentioning

confidence: 99%

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Ghumman,

Singh

2020

IJEAT

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Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

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“…Feature extraction method used was basically common spatial patterns; the advantage of using this feature extraction method is basically of twoclass discrimination problems. is maximizes the variance of one class and decreases the variance of the other class, which is the advantage, but the disadvantage is because of the multiclass overlap structure in this method, it is not used for multiclass prediction [6]. 6 Table 2 shows the comparison of classification algorithms.…”

Section: Critical Review Of the Related Literaturementioning

confidence: 99%

Deep Learning Algorithm for Brain-Computer Interface

Mansoor

Usman

Jamil

et al. 2020

Scientific Programming

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Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms.

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Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

Cited by 66 publications

References 23 publications

Development of a Modular Board for EEG Signal Acquisition

Development of a Modular Board for EEG Signal Acquisition

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Deep Learning Algorithm for Brain-Computer Interface

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