Brain-computer interface as measurement and control system the review paper

Rak, Remigiusz J.; Kołodziej, Marcin; Majkowski, Andrzej

doi:10.2478/v10178-012-0037-4

Cited by 34 publications

(22 citation statements)

References 33 publications

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“…Moreover, during acquisition process the EDADC removes the low amplitude noise across the signal base line. This phenomenon is called the noise thresholding and it improves the post signal analysis and classification accuracy [4][5][6]. Furthermore, an additional signal enhancement is achieved with the de-noising process, realized with an adaptive rate filtering method [11].…”

Section: Resultsmentioning

confidence: 99%

“…For the studied case, the FIR filters cut-off frequencies are chosen as [F CDmin =0; F CDmax =55]Hz. It effectively de-noises the intended signal [5,6].…”

Section: The Adaptive Rate Filteringmentioning

confidence: 99%

“…The de-noising process enhance the segmented portion signal to noise ratio (SNR). It improves the post analysis and classification modules precision [5,6].…”

Section: Introductionmentioning

confidence: 99%

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An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals

Qaisar¹,

Ramadan²,

Subaşı³

2018

Preprint

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The segmentation and de-noising are basic operations, required in every signal processing and classification system. The classical segmentation and de-noising approaches are time-invariant. Consequently, it results in the post processing of an unnecessary information and causes an increase in the system processing activity and power consumption. In this context, an efficient event-driven segmentation and de-noising technique is proposed. It is founded on the principles of level crossing and activity selection. Therefore, it can adapt its sampling frequency, segmentation window length and position along with the filter order by analyzing the input signal local characteristics. As a result, the computational complexity and the power consumption of the proposed system is reduced compared to the counter ones. The suggested system performance is compared with the classical one. It is done for the case of a multi-channel Electroencephalogram (EEG) signals. Results show a noticeable compression gain with an effective adaptation of the de-noising filters order. It aptitudes a significant computational gain, transmission data rate reduction and power consumption reduction of the proposed technique, compared to the counter ones. It shows that the proposed solution is an attractive candidate to embed in the new generation EEG wearables.   

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Section: Resultsmentioning

confidence: 99%

“…For the studied case, the FIR filters cut-off frequencies are chosen as [F CDmin =0; F CDmax =55]Hz. It effectively de-noises the intended signal [5,6].…”

Section: The Adaptive Rate Filteringmentioning

confidence: 99%

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An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals

Qaisar¹,

Ramadan²,

Subaşı³

2018

Preprint

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“…The general structure of the processing system is shown in Fig 3. It has three main components, i.e. pre-processing, extraction, and feature selection [33]. All data from the available EEG channels are given as input data.…”

Section: P300 Paradigmmentioning

confidence: 99%

“…The result of the extraction is the matrix of features representing the EEG signal. Feature selection eliminates unnecessary features prior to the classification process [33].…”

Section: P300 Paradigmmentioning

confidence: 99%

Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

2019

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The main goal of the paper is to perform a comparative accuracy analysis of the two-group classification of EEG data collected during the P300-based brain-computer interface tests. The brain-computer interface is a technology that allows establishing communication between a human brain and external devices. BCIs may be applied in medicine to improve the life of disabled people and as well for entertainment. The P300 is an event-related potential (ERP) appearing about 300 ms after the occurrence of the stimulus of visual, auditory or sensory nature. It is based on the phenomenon observed in anticipation for a target event among non-target events. The 21-channel 201 Mitsar amplifier was used during the experiment to store EEG data from seven electrodes placed on the dedicated cap. The study was conducted on a group of five persons using P300 scenario available in OpenVibe software. The experiment was based on three steps the classifier learning process, comparison and averaging of the obtained result and the final test of the classifier. The comparative analysis was performed with the application of two supervised classification methods: Linear Discriminant Analysis (LDA) and Multi-layer Perceptron (MLP). The preliminary data analysis, extraction and feature selection was performed prior to the classification.

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Feature Extraction and Classification in Brain-Computer Interfacing: Future Research Issues and Challenges

Chakladar

Chakraborty²

2018

Unsupervised and Semi-Supervised Learning

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Brain-computer interface as measurement and control system the review paper

Cited by 34 publications

References 33 publications

An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals

An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals

Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

Feature Extraction and Classification in Brain-Computer Interfacing: Future Research Issues and Challenges

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