Fractal dimension based neurofeedback training to improve cognitive abilities

Liu, Yisi; Hou, Xiyuan; Sourina, Olga

doi:10.1109/ceec.2015.7332716

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…The characteristics of interest (features) extracted from the brain recordings have to represent the brain patterns that one wants to modulate and provide feedback based on. In EEG, these features are often spectral bands of EEG signals (Egner & Gruzelier, 2004;Grosse-Wentrup & Schölkopf, 2014;Liu, Hou, & Sourina, 2015;Zhigalov, Kaplan, & Palva, 2016;Wei et al, 2017) or eventrelated potential (ERP) components of the signal (Farwell & Donchin, 1988;Treder & Blankertz, 2010;Zhang, Zhao, Jin, Wang, & Cichocki, 2012). Over the past 20 years, the number of journal articles about machine learning in neuroscience has grown continuously (Glaser, Benjamin, Farhoodi, & Kording, 2019).…”

Section: Neurofeedback System Componentsmentioning

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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Section: Neurofeedback System Componentsmentioning

confidence: 99%

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

et al. 2021

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“…Benioudakis et al [13] used game content as a stimulus for neurofeedback training (at P4-T4, T3-T4, Fp2-T4, Fp1-T3 for delta and theta) to reduce anxiety and depression in post-trauma cancer patients, and significant improvement was noted. Liu et al [14], [43] investigated neurofeedback training (at P8) to improve the cognitive abilities by using a shooting game as a stimulus. Farkas et al [44] described game content as a stimulus of neurofeedback training and suggested that neurofeedback is most effective for the comorbid Gilles-de-la-Tourette syndrome.…”

Section: Gamesmentioning

confidence: 99%

“…The selection of suitable neurofeedback stimulus contents has often been challenging, with various researchers having investigated different neurofeedback stimulus contents (e.g. color bar changer [12], audio [6], video [13], and games [14]) for peak-performance training [15], cognitive enhancement in healthy participants, and to influence symptoms in patients [16], [17]. In addition, the neurofeedback stimulus contents play a critical role in mitigating stress [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Investigating Neurofeedback Protocols for Stress Mitigation: A Comparative Analysis of Different Stimulus Contents

et al. 2019

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The selection of stimulus contents for neurofeedback has direct implications on the efficacy of neurofeedback therapy. In particular, a suitable selection of stimulus contents facilitates the achievement of sustainability during neurofeedback sessions, which has been considered challenging during clinical practice. To further elaborate this point, this research investigates the efficacy of different neurofeedback stimulus contents (audio, video, and games) for stress mitigation. The effectiveness of the contents was measured by statistically comparing quantitative electroencephalogram (QEEG) features, such as alpha power and alpha asymmetry before and after neurofeedback sessions. In addition, the topographic maps of activities were constructed for a visual description. In this study, 29 study participants were recruited, and the EEG data were recorded during multiple neurofeedback sessions. ANOVA and post hoc testing verified the statistical significance of the results of the various stimulus contents, whereas a t-test verified the significance of stress mitigation because of neurofeedback. The results indicate that games exhibit higher effectiveness than audio and video contents for stress mitigation. In addition, the topographic analysis demonstrates the efficacy of neurofeedback training for stress mitigation. In conclusion, the effects of neurofeedback therapy could be enhanced while selecting suitable stimulus contents for neurofeedback protocols.INDEX TERMS Neurofeedback stimulus contents, QEEG analysis, neurofeedback for stress mitigation.

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“…The characteristics of interest (features) extracted from the brain recordings have to represent the brain patterns that one wants to modulate and provide feedback based upon. In EEG, these features are often spectral bands of EEG signals [72,73,74,75,76] or event related potentials (ERP) components of the signal [77,78,79]. Over the past 20 years, the number of journal articles about machine learning in neuroscience have grown continuously [80].…”

Section: Neurofeedback System Componentsmentioning

confidence: 99%

“…The goal of neurofeedback training is often uptraining of specific spectral bands (e.g. [72,73,74,75,76]), hence diminishing the need for feature extraction besides identification of the spectral component of interest. This approach is not optimal for neurofeedback paradigms that aim to decode more complex cognitive states, as it diminishes the possibility of using potentially relevant neural processing taking place in spectral bands other than the preselected band.…”

Section: Challenges and Prospects For Neurofeedback Systemsmentioning

confidence: 99%

A framework for closed-loop neurofeedback for real-time EEG decoding

Tuckute

Kjaer

et al. 2019

Preprint

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Neurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroencephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.The software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenter's needs.As a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre-and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.The proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

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Fractal dimension based neurofeedback training to improve cognitive abilities

Cited by 8 publications

References 22 publications

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

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

Investigating Neurofeedback Protocols for Stress Mitigation: A Comparative Analysis of Different Stimulus Contents

A framework for closed-loop neurofeedback for real-time EEG decoding

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