Design and Preliminary Study of a neurofeedback Protocol to Self-Regulate an EEG Marker of Drowsiness

Neurofeedback (NF) consists in using electroencephalographic (EEG) measurements to guide users to perform a cognitive learning using information coming from their own brain activity, by means of a realtime sensory feedback (e.g., visual or auditory). Many NF approaches have been studied to improve attentional abilities, notably for attention deficit hyper activity disorder. However, to our knowledge, no NF solution has been proposed to specifically reduce drowsiness. Thus, we propose an EEG-NF solution to train users to selfregulate an EEG marker of drowsiness, and evaluate it with a preliminary study. Results with five healthy subjects showed that three of them could learn to self-regulate this EEG marker with a relatively short number of NF sessions (up to 8 sessions of 40 min). Clinical trials with sleep-deprived subjects should begin in 2019 to study possible cognitive and clinical benefits of this self-regulation. This NF solution implementation is available for free, with the OpenViBE platform, under the AGPL-3.0 license.

[1]  Guillaume Gibert,et al.  OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments , 2010, PRESENCE: Teleoperators and Virtual Environments.

[2]  A. Wirz-Justice,et al.  Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness. , 1995, Sleep.

[3]  Fabien Lotte,et al.  Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review , 2018, Neuroscience.

[4]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[5]  M. Arns,et al.  Evaluation of neurofeedback in ADHD: The long and winding road , 2014, Biological Psychology.

[6]  Chi Thanh Vi,et al.  Continuous Tactile Feedback for Motor-Imagery Based Brain-Computer Interaction in a Multitasking Context , 2015, INTERACT.

[7]  I. Jaussent,et al.  Incidence, worsening and risk factors of daytime sleepiness in a population-based 5-year longitudinal study , 2017, Scientific Reports.

[8]  M Congedo,et al.  Neurofeedback: One of today's techniques in psychiatry? , 2017, L'Encephale.

[9]  Camille Jeunet,et al.  Using EEG-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: Theoretical background, applications and prospects , 2019, Neurophysiologie Clinique.

[10]  F Lotte,et al.  Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates. , 2016, Progress in brain research.

[11]  Pierre Philip,et al.  Excessive daytime sleepiness in adult patients with ADHD as measured by the Maintenance of Wakefulness Test, an electrophysiologic measure. , 2015, The Journal of clinical psychiatry.

[12]  Fabien Lotte,et al.  On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms? , 2017, Brain : a journal of neurology.

[13]  Suzanna Becker,et al.  Progressive Thresholding: Shaping and Specificity in Automated Neurofeedback Training , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Sriram Subramanian,et al.  Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns , 2015, PloS one.

[15]  J. Leon Kenemans,et al.  Neurofeedback in ADHD and insomnia: Vigilance stabilization through sleep spindles and circadian networks , 2014, Neuroscience & Biobehavioral Reviews.

[16]  F. Bartolomei,et al.  Electroencephalographic neurofeedback: Level of evidence in mental and brain disorders and suggestions for good clinical practice , 2015, Neurophysiologie Clinique/Clinical Neurophysiology.

[17]  Pierre Philip,et al.  EEG neurofeedback treatments in children with ADHD: an updated meta-analysis of randomized controlled trials , 2014, Front. Hum. Neurosci..

[18]  Jarrod A. Lewis-Peacock,et al.  Closed-loop brain training: the science of neurofeedback , 2017, Nature Reviews Neuroscience.

[19]  Robert T. Thibault,et al.  Neurofeedback or neuroplacebo? , 2017, Brain : a journal of neurology.

[20]  Robert T. Thibault,et al.  Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist) , 2019, Brain : a journal of neurology.

[21]  Sing-Yau Goh,et al.  Effect of mindfulness meditation on brain–computer interface performance , 2014, Consciousness and Cognition.

[22]  F. Cabestaing,et al.  EEG neurofeedback research: A fertile ground for psychiatry? , 2019, L'Encephale.

[23]  Robert T. Thibault,et al.  The Psychology of Neurofeedback: Clinical Intervention Even if Applied Placebo , 2017, The American psychologist.

[24]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.

[25]  Jean-Arthur Micoulaud-Franchi,et al.  A framework for disentangling the hyperbolic truth of neurofeedback: Comment on Thibault and Raz (2017). , 2018, The American psychologist.