Home-Based EEG Neurofeedback Intervention for the Management of Chronic Pain

Soh

Child Adolesc Psychiatry Ment Health

et al. 2023

Background Attention deficit hyperactivity disorder (ADHD) is a prevalent child neurodevelopmental disorder that is treated in clinics and in schools. Previous trials suggested that our brain–computer interface (BCI)-based attention training program could improve ADHD symptoms. We have since developed a tablet version of the training program which can be paired with wireless EEG headsets. In this trial, we investigated the feasibility of delivering this tablet-based BCI intervention at home. Methods Twenty children diagnosed with ADHD, who did not receive any medication for the preceding month, were randomised to receive the 8-week tablet-based BCI intervention either in the clinic or at home. Those in the home intervention group received instructions before commencing the program and got reminders if they were lagging on the training sessions. The ADHD Rating Scale was completed by a blinded clinician at baseline and at week 8. Adverse events were monitored during any contact with the child throughout the trial and at week 8. Results Children in both groups could complete the tablet-based intervention easily on their own with minimal support from the clinic therapist or their parents (at home). The intervention was safe with few reported adverse effects. Clinician-rated inattentive symptoms on the ADHD-Rating Scale reduced by 3.2 (SD 6.20) and 3.9 (SD 5.08) for the home-based and clinic-based groups respectively, suggesting that home-based intervention was comparable to clinic-based intervention. Conclusions This trial demonstrated that the tablet version of our BCI-based attention training program can be safely delivered to children in the comfort of their own home. Trial registration This trial is registered at clinicaltrials.gov as NCT01344044

Section: Discussionsupporting

confidence: 87%

Home-based brain–computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial

Soh

Child Adolesc Psychiatry Ment Health

et al. 2023

“…3). The current selection of studies used heart rate sensors (n = 6) and respiratory sensors (n = 5), with 1 study using a wearable electroencephalogram (EEG) 8 and 1 study using a commercial smart bracelet to detect electrodermal activity. 52…”

Section: Resultsmentioning

confidence: 99%

“…This specific use case allows control of some crucial aspects of the EEG recording session (ie, participant static in a room with constant lighting and following precise instructions on a computer screen). However, only 1 study 8 discussed EEG data in relation to psychological or social variables, and in this case, theta (4–8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and hi-beta (20-30 Hz) frequency bands were used for biofeedback. Decreased activity in relative hi-beta was associated with decreased anxiety and depression levels.…”

Section: Resultsmentioning

confidence: 99%

Sensing behavior change in chronic pain: A scoping review of sensor technology for use in daily life

Vitali,

Olugbade,

Eccleston

et al. 2024

Pain

Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided “ground truth” for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.

“…The home-based NFT led to improvements in sleep quality ( Krepel et al, 2022 ). Birch et al (2022) trained their participants remotely via a video link before the beginning of the NFT until they were able to mount the EEG headset, minimize EEG artifacts, and complete a full training session on their own. The home-based NFT sessions were performed without assistance from a supervising NF expert.…”

Section: Introductionmentioning

confidence: 99%

“…The home-based NFT sessions were performed without assistance from a supervising NF expert. Home-based NFT resulted in a significant pain reduction in patients with chronic pain ( Birch et al, 2022 ). The results of these studies provide evidence for the successful regulation of one’s own brain activity through home-based NFT.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the capacity to modulate brain signals in a home-based SMR neurofeedback training setting

Autenrieth

Kober

Wood

2023

Front. Hum. Neurosci.

Electroencephalogram (EEG)-based neurofeedback (NF) is mainly used in clinical settings as a therapeutic intervention or to optimize performance in healthy individuals. Home-based NF systems are available and might facilitate general access to NF training, especially when repeated training sessions are necessary. However, it remains an open question whether NF training at home is possible without remote monitoring. In the present study, we assessed the capacity of healthy individuals to modulate their own EEG activity when using a home-based NF training system in a comparable manner as if participants had purchased a commercially available NF system. Participants’ face-to-face contact with experimenters was reduced to a minimum, and instructions were provided only in the form of written information or videos. Initially, 38 participants performed 9 sessions of sensorimotor rhythm (SMR) (12–15 Hz) based NF training (three generalization sessions, six training sessions). An active control group (n = 19) received feedback on random EEG frequencies. Because of technical problems, bad EEG data quality, or non-compliance, 21 participants had to be excluded from the final data analysis, providing first evidence for the difficulties of non-supervised home-based NF training. In this study, participants were not able to modulate their own brain activity in a desired direction during NF training. Our results indicate that personal interaction with a NF expert might be of relevance and that remote supervision of the training data and more direct communication with the NF users are necessary to enable successful NF training performance. We provide suggestions for the development and implementation of home-based NF systems.