Auditory Evoked Potential (AEP) Based Brain-Computer Interface (BCI) Technology: A Short Review

Islam, Nahidul; Sulaiman, Norizam; Bari, Bifta Sama; Rashid, Mamunur; Mustafa, Mahfuzah

doi:10.1007/978-3-030-70917-4_26

Cited by 2 publications

(2 citation statements)

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“…It is clear that MI techniques often yield impressive results when individuals have been properly trained. For a targeted user to handle an MI-based BCI, the targeted user requires several training trails, making the calibration time unacceptable to a realistic model [7]. P300 produces larger average ITRs and does not require training, but it significantly affects the severity and variability of disease performance..…”

Section: Introductionmentioning

confidence: 99%

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A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology

et al. 2021

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The patients who are impaired with neurodegenerative disorders cannot command their muscles through the neural pathways. These patients are given an alternative from their neural path through Brain-Computer Interface (BCI) systems, which are the explicit use of brain impulses without any need for a computer's vocal muscle. Nowadays, the steady-state visual evoked potential (SSVEP) modality offers a robust communication pathway to introduce a non-invasive BCI. There are some crucial constituents, including window length of SSVEP response, the number of electrodes in the acquisition device and system accuracy, which are the critical performance components in any BCI system based on SSVEP signal. In this study, a real-time hybrid BCI system consists of SSVEP and EMG has been proposed for the environmental control system. The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. The obtained classification accuracy of eight participants was 97.41%. Finally, a control mechanism that aims to apply for the environmental control system has also been developed. The proposed system can identify 18 commands (i.e., 16 control commands using SSVEP and two commands using EMG). This result represents very encouraging performance to handle real-time SSVEP based BCI system consists of a small number of electrodes. The proposed framework can offer a convenient user interface and a reliable control method for realistic BCI technology.

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Section: Introductionmentioning

confidence: 99%

“…The stimulation method is so complicated that patients can not carry out the experimental process. In addition, a large variety of instructions in a BCI system based on P300 enhances the number of trails, which in turn lessens the overall outcomes [7].…”

Section: Introductionmentioning

confidence: 99%