Feedback delay and amplitude threshold and control of the occipital EEG

Mulholland, Thomas; Boudrot, Robert; Anne, Davidson

doi:10.1007/bf01007104

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…Accordingly, participants had three delay conditions as a between-session factor and five target conditions as a within-session factor. Previous studies have suggested that approximately a 1-s delay or epoching may be long enough to disturb or reduce the effect of EEG-NF (Mulholland et al, 1979;Belinskaya et al, 2020). Furthermore, a 20-s delay was intended to make a total discrepancy between the current and target state since the target moves every 20 s (for example, participants see the target msA then try to make an msA state of their own, but the presented visual feedback was for the previous target condition other than msA).…”

Section: Inserting Delay For System Validationmentioning

confidence: 99%

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

Asai

Hamamoto

Kashihara

et al. 2022

Front. Syst. Neurosci.

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Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. EEG-microstates (EEGms) are spatially featured states that can be regulated through NF training, given that they have recently been indicated as biomarkers for some disorders. The current study was conducted to develop an EEG-NF system for detecting “canonical 4 EEGms” in real time. There are four representative EEG states, regardless of the number of channels, preprocessing procedures, or participants. Accordingly, our 10 Hz NF system was implemented to detect them (msA, B, C, and D) and audio-visually inform participants of its detection. To validate the real-time effect of this system on participants’ performance, the NF was intentionally delayed for participants to prevent their cognitive control in learning. Our results suggest that the feedback effect was observed only under the no-delay condition. The number of Hits increased significantly from the baseline period and increased from the 1- or 20-s delay conditions. In addition, when the Hits were compared among the msABCD, each cognitive or perceptual function could be characterized, though the correspondence between each microstate and psychological ability might not be that simple. For example, msD should be generally task-positive and less affected by the inserted delay, whereas msC is more delay-sensitive. In this study, we developed and validated a new EEGms-NF system as a function of delay. Although the participants were naive to the inserted delay, the real-time NF successfully increased their Hit performance, even within a single-day experiment, although target specificity remains unclear. Future research should examine long-term training effects using this NF system.

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Section: Inserting Delay For System Validationmentioning

confidence: 99%

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

Asai

Hamamoto

Kashihara

et al. 2022

Front. Syst. Neurosci.

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“…As in the present investigation, the system of Lal et al uses AR-computed spectral features of brain signals recorded over a period of a few seconds for binary classification. Unlike the present system, their system does not provide continuous feedback of brain signal features but displays the classification result ("feedback of results") with a delay of 3 s. For self-regulation of brain states, it has been shown for EEG that timely feedback is important to facilitate learning (Mulholland et al, 1979;Rockstroh et al, 1990), so we implemented a feedback system with minimal delay. In the paper of Lal et al, no classification rates or other data are given that allow a judgement of on-line performance or learning rates.…”

Section: Immediate Feedbackmentioning

confidence: 99%

An MEG-based brain–computer interface (BCI)

et al. 2007

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Brain-Computer Interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG, and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor μ and β rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant μ-rhythm self control within 32 minutes of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

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“…However, in LED mode, this alpha component was not observed. Since the alpha waves are in close relationship with the degree of the attention (as pointed by Dewan [30] and Mulholland et al [31], there was a significant inverse relationship between alpha wave and attention), these results might imply that LCD would result in less attention. In LCD mode, the monitor needs to be refreshed at given frequency and it has the gray to gray (GTG) response time (gray to gray is a response time of the monitor from one gray level to another gray level).…”

Section: Discussionmentioning

confidence: 80%

Stimulator Selection in SSVEP-Based Spatial Selective Attention Study

Xie

Chang

Obermayer³

et al. 2016

Computational Intelligence and Neuroscience

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Steady-State Visual Evoked Potentials (SSVEPs) are widely used in spatial selective attention. In this process the two kinds of visual simulators, Light Emitting Diode (LED) and Liquid Crystal Display (LCD), are commonly used to evoke SSVEP. In this paper, the differences of SSVEP caused by these two stimulators in the study of spatial selective attention were investigated. Results indicated that LED could stimulate strong SSVEP component on occipital lobe, and the frequency of evoked SSVEP had high precision and wide range as compared to LCD. Moreover a significant difference between noticed and unnoticed frequencies in spectrum was observed whereas in LCD mode this difference was limited and selectable frequencies were also limited. Our experimental finding suggested that average classification accuracies among all the test subjects in our experiments were 0.938 and 0.853 in LED and LCD mode, respectively. These results indicate that LED simulator is appropriate for evoking the SSVEP for the study of spatial selective attention.

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Feedback delay and amplitude threshold and control of the occipital EEG

Cited by 10 publications

References 7 publications

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

An MEG-based brain–computer interface (BCI)

Stimulator Selection in SSVEP-Based Spatial Selective Attention Study

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