Mind your thoughts: BCI using single EEG electrode

Narayana, Sujay; Prasad, R. Venkatesha; Warmerdam, Kevin

doi:10.1049/iet-cps.2018.5059

Cited by 22 publications

(11 citation statements)

References 13 publications

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“…Several studies have examined the use of baseline EEG signals to consider the different participant characteristics on experimental signals [39], [40]. It is important to note that the baseline EEG signals represent a calm state before a stimulus medium is given [25], [28], [31], [41], [42]. The steps of the baseline approach are cutting all the C channels in the baseline signals into several N segments with length L, and each segment is averaged to obtain the 𝐵𝑎𝑠𝑒𝑀𝑒𝑎𝑛 value using the (2).…”

Section: Rq 2: How Can An Eeg Signal Be Generated With Consideration Of Differences In Participant Characteristics?mentioning

confidence: 99%

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

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Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

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Section: Rq 2: How Can An Eeg Signal Be Generated With Consideration Of Differences In Participant Characteristics?mentioning

confidence: 99%

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

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“…Hence, closed-loop neurofeedback allows for interference with brain activity in real time and thereby enables investigations of how brain network dynamics contribute to task performance in a causal manner, making it a powerful neuroscientific tool (Kangassalo, Spapé, & Ruotsalo, 2020, for reviews, see Bagdasaryan & Le Van Quyen, 2013;Stoeckel et al, 2014). Moreover, based on recent methodological and technical advances in brain-computer interfaces (BCI), there is increasing interest in using neurofeedback for cognitive training, especially if it is employed in accessible and user-friendly modalities (Narayana, Prasad, & Warmerdam, 2019;Jafri et al, 2019, for recent reviews, see Baek, Chang, Heo, & Park, 2019;Kosmyna & Lécuyer, 2019;Lupu, Ungureanu, & Cimpanu, 2019).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback

et al. 2021

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Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback. During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance [Formula: see text] 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction [Formula: see text]e[Formula: see text]4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities. We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.

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“…EEG signals are known to exhibit certain rhythmic characteristics while performing a particular task. These rhythms have been analysed in many BCI tasks such as detecting different types of sleep disorders [8] and to study meditative and attentive states of the human mind [9].…”

Section: Introductionmentioning

confidence: 99%

Wavelet filterbank‐based EEG rhythm‐specific spatial features for covert speech classification

Biswas

Sinha

2021

IET Signal Processing

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The derivation of rhythm-specific spatial patterns of electroencephalographic (EEG) signals for covert speech EEG classification task is dealt in this work. This study has been performed on a publicly accessible multi-channel covert speech EEG database consisting of multi-syllabic words. With the motivation of deriving more discriminative features, each channel data has been decomposed into distinct bands focussing on the five basic EEG rhythms using the discrete wavelet transform (DWT)-based signal decomposition algorithm. Following that, for each band, the multi-class common spatial pattern (CSP) features are computed using joint approximate diagonalisation. The final feature vector is formed by retaining a few significant CSP components from all five bands. Radial basis function kernel-based support vector machines are used for covert speech classification. After 5-fold cross-validation, the proposed DWT-based bandwise-CSP features are noted to yield an average classification accuracy of 94%. In contrast with the existing (nondecomposed) CSP feature, a relative improvement of about 24% is achieved. For generalisation purposes, the proposed approach has also been evaluated for another covert speech database comprising more classes and subjects. The study highlights the discovery of more discriminative patterns with rhythm-specific processing in the context of covert speech classification. The proposed approach has the potential to be useful in other brain-computer interface paradigms that employ EEG signals.

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Mind your thoughts: BCI using single EEG electrode

Cited by 22 publications

References 13 publications

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback

Wavelet filterbank‐based EEG rhythm‐specific spatial features for covert speech classification

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