Vocal Imagery vs Intention: Viability of Vocal-Based EEG-BCI Paradigms

Kristensen, Alexander Borch; Subhi, Younes; Puthusserypady, Sadasivan

doi:10.1109/tnsre.2020.3004924

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…A recent study shows that a new system dimension controls the categories of people with speech problems and with ordinary people to assist them in everyday life by humming [44]. This article revealed the feasibility of EEG in BCI with vocal Imagery and vocal Intention.…”

Section: Eeg Application In Bcimentioning

confidence: 78%

A Review on Applications of Electroencephalogram: Includes Imagined Speech

Santhakumari,

2024

IJACSA

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In the last two decades, the Brain-Computer Interface system with EEG signals has assisted people in various ways. In particular, to patients with paralysis, epilepsy, and Alzheimer's disease, not only to the patient but also to physically, visually challenged people and Hard-of-Hearing people. One of the non-invasive methods that can read human brain activities is Electroencephalogram (EEG). The EEG has been used in many applications, especially in medicine. The applications of the EEG are not limited to the medical domain; it keeps extending to many areas. This review includes the various application of EEG; and more in imagined speech. The main objective of this survey is to know about imagined speech, and perhaps to some extent, will be useful future direction in decoding imagined speech. Imagined speech classifications have used different models; the models are discussed, the significance of choosing the number of electrodes, and the main challenges in EEG.

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Section: Eeg Application In Bcimentioning

confidence: 78%

A Review on Applications of Electroencephalogram: Includes Imagined Speech

Santhakumari,

2024

IJACSA

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“…These commands are applied to various applications such as mental spellers (Hwang et al, 2012;Lim et al, 2015;Speier et al, 2017), assistive technology for patients (Hwang et al, 2017;Lim et al, 2017), and online home appliance control (Park et al, 2020). EEG-based BCIs can be classified based on the paradigms to elicit neural activities (Hwang et al, 2013) as motor imagery (MI)-based BCI (Acqualagna et al, 2016;León et al, 2020), visual P300-based BCI (Gu et al, 2019), steady-state visual evoked potential (SSVEP)-based BCI, non-motor mental imagery-based BCI (Kristensen et al, 2020), auditory BCI (Kim et al, 2011;Simon et al, 2015), and hybrid BCI (Jalilpour et al, 2020). Among these, SSVEP-based BCIs have the advantage of higher accuracy and higher information transfer rate (ITR) and generally require no/short training time (Tello et al, 2014;Nakanishi et al, 2015;Hwang et al, 2017;Xing et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

Kim

2021

Front. Neuroinform.

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There remains an active investigation on elevating the classification accuracy and information transfer rate of brain-computer interfaces based on steady-state visual evoked potential. However, it has often been ignored that the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can be affected through the minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments involved 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the occipital area of the participants. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1–3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, EMSI outperformed MSI when comparing the ACA and RES values under the multichannel conditions. In conclusion, our results suggested that the use of multichannel configuration and employment of EMSI could make the performance of SSVEP-based BCIs more robust to the electrode shift from the optimal locations.

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“…Recent advances have led to notable success in improving language content decoding directly from EEG. For example, the neural correlates of vowels, consonants, phonemes, syllables, and even words have been classified using advanced decoding algorithms [19], [20]. Despite the promising results achieved to date, EEG-based speech imagery decoding is still a potential field because it has several limitations.…”

mentioning

confidence: 99%

Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain–Machine Interaction

Jeong

Cho

Lee

et al. 2023

IEEE Trans. Cybern.

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Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through realtime experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.

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Vocal Imagery vs Intention: Viability of Vocal-Based EEG-BCI Paradigms

Cited by 7 publications

References 40 publications

A Review on Applications of Electroencephalogram: Includes Imagined Speech

A Review on Applications of Electroencephalogram: Includes Imagined Speech

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain–Machine Interaction

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