SMR neurofeedback shows potential as a therapeutic tool for reducing sleep problems. It is hypothesized that SMR neurofeedback trains the reticulo-thalamocortical-cortical circuit involved in sleep-spindle generation. As such, strengthening this circuit is hypothesized to reduce sleep problems. The current study aims to investigate the effectiveness of a home-based device that uses SMR neurofeedback to help reduce sleep problems. Thirty-seven participants reporting sleep problems received the SMR neurofeedback-based program for 40 (n = 21) or 60 (n = 16) sessions. The Pittsburgh Sleep Quality Index (PSQI) and Holland Sleep Disorders Questionnaire (HSDQ) were assessed at baseline, session 20, outtake, and follow-up (FU). Actigraphy measurements were taken at baseline, session 20, and outtake. Significant improvements were observed in PSQI Total (d = 0.78), PSQI Sleep Duration (d = 0.52), HSDQ Total (d = 0.80), and HSDQ Insomnia (d = 0.79). Sleep duration (based on PSQI) increased from 5.3 h at baseline to 5.8 after treatment and 6.0 h. at FU. No effects of number of sessions were found. Participants qualified as successful SMR-learners demonstrated a significantly larger gain in sleep duration (d = 0.86 pre-post; average gain = 1.0 h.) compared to non-learners. The home-based SMR tele-neurofeedback device shows the potential to effectively reduce sleep problems, with SMR-learners demonstrating significantly better improvement. Although randomized controlled trials (RCTs) are needed to further elucidate the specific effect of this device on sleep problems, this is the first home-based SMR neurofeedback device using dry electrodes demonstrating effectiveness and feasibility.

Introduction: SMR neurofeedback shows potential as a therapeutic tool for reducing sleep problems. It is hypothesized that SMR neurofeedback trains the reticulo-thalamocortical-cortical circuit involved in sleep-spindle generation. As such, strengthening this circuit is hypothesized to reduce sleep problems. The current study aims to investigate the effectiveness of a home-based device that uses SMR neurofeedback to help reduce sleep problems. Methods: Thirty-seven participants reporting sleep problems received the SMR neurofeedback-based program for 40 (n = 21) or 60 (n = 16) sessions. The Pittsburgh Sleep Quality Index (PSQI) and Holland Sleep Disorders Questionnaire (HSDQ) were assessed at baseline, session 20, outtake, and follow-up (FU). Actigraphy measurements were taken at baseline, session 20, and outtake. Results: Significant improvements were observed in PSQI Total (d = 0.78), PSQI Sleep Duration (d = 0.52), HSDQ Total (d = 0.80), and HSDQ Insomnia (d = 0.79). Sleep duration (based on PSQI) increased from 5.3 hours at baseline to 5.8 after treatment and 6.0 hrs. at FU. No effects of number of sessions were found. Participants qualified as successful SMR-learners demonstrated a significantly larger gain in sleep duration (d = 0.86 pre-post; average gain = 1.0 hrs.) compared to non-learners. Conclusions: The home-based SMR tele-neurofeedback device shows the potential to effectively reduce sleep problems, with SMR-learners demonstrating significantly better improvement. Although randomized controlled trials (RCTs) are needed to further elucidate the specific effect of this device on sleep problems, this is the first home-based SMR neurofeedback device using dry electrodes demonstrating effectiveness and feasibility.

Sensorimotor (SMR) neurofeedback is a promising therapy for several health disorders but is still not widely used due to the high cost of the equipment. URGOnight offers a low-cost solution to democratize these therapies by providing an at-home EEG headband with dry electrodes connected to a mobile application. The first aim of this study is both to validate the URGOnight EEG signal and to compare it to Enobio-20, a medical grade EEG device. The second aim of the study is to propose a new method to detect SMR rhythm based on its oscillatory properties and discriminate it from alpha oscillations. In our study, we compared the URGOnight headband EEG signal (C3/C4) to Enobio- 20 (CP3/CP4), placed on subjects simultaneously equipped with the two headbands. All subjects (n=33) performed a dual blocking task inspired by Kulhman (1978) based on the blocking effect of movement and eyes opening on SMR and alpha respectively. This task was followed by SSVEP stimulations to evaluate the frequency response of the two EEG devices. The performance of the EEG headbands was statistically identical for most of the characteristics of the EEG signal, including the frequency response to SSVEP (from 4Hz to 20Hz). The main difference was a larger amplitude in the 8-15Hz due to the location of the reference in URGOnight that did not impair the detection of both alpha and SMR. In addition, we show that our new method allows to discriminate alpha and SMR rhythms based on their oscillatory properties with a single recording site (C3/C4). The method is fast enough to be used in real time. We show that the detected SMR rhythm is modulated by movement as opposed to the 12-15Hz frequency band often used as indicator of SMR in most neurofeedback studies. Altogether, our results validate the quality of the EEG recordings obtained with URGOnight since it gives similar results as the one obtained with Enobio-20, a validated EEG medical grade system. In addition, we provide a new method allowing the identification and the separation of the alpha and SMR with a single recording site C3/C4. This method opens up a new research lead to improve SMR neurofeedback efficiency and thus of its clinical possibilities by focusing on the reinforcement of the SMR oscillation strictly speaking.