A randomized controlled trial of a brain-computer interface based attention training program for ADHD

Abstract

Objective The use of brain-computer interface in neurofeedback therapy for attention deficit hyperactivity disorder (ADHD) is a relatively new approach. We conducted a randomized controlled trial (RCT) to determine whether an 8-week brain computer interface (BCI)-based attention training program improved inattentive symptoms in children with ADHD compared to a waitlist-control group, and the effects of a subsequent 12-week lower-intensity training. Study design We randomized 172 children aged 6–12 attending an outpatient child psychiatry clinic diagnosed with inattentive or combined subtypes of ADHD and not receiving concurrent pharmacotherapy or behavioral intervention to either the intervention or waitlist-control group. Intervention involved 3 sessions of BCI-based training for 8 weeks, followed by 3 training sessions per month over the subsequent 12 weeks. The waitlist-control group received similar 20-week intervention after a wait-time of 8 weeks. Results The participants’ mean age was 8.6 years (SD = 1.51), with 147 males (85.5%) and 25 females (14.5%). Modified intention to treat analyzes conducted on 163 participants with at least one follow-up rating showed that at 8 weeks, clinician-rated inattentive symptoms on the ADHD-Rating Scale (ADHD-RS) was reduced by 3.5 (SD 3.97) in the intervention group compared to 1.9 (SD 4.42) in the waitlist-control group (between-group difference of 1.6; 95% CI 0.3 to 2.9 p = 0.0177). At the end of the full 20-week treatment, the mean reduction (pre-post BCI) of the pooled group was 3.2 (95% CI 2.4 to 4.1). Conclusion The results suggest that the BCI-based attention training program can improve ADHD symptoms after a minimum of 24 sessions and maintenance training may sustain this improvement. This intervention may be an option for treating milder cases or as an adjunctive treatment.

Attention-deficit/hyperactivity disorder symptoms in children with surgically corrected Ventricular Septal Defect, Transposition of the Great Arteries, and Tetralogy of Fallot

“…These findings are in line with other studies 38,39 and highlight the importance of early identification and treatment of attention-deficit/hyperactivity disorder in CHD, in order to improve quality of life for these children and their families. 40 The Attention-Deficit/Hyperactivity Disorder-Rating Scale measures attention-deficit/hyperactivity disorder in children from 6 to 16 years and is feasible to be used in a clinical setting as a screening tool, 41 for example, in a CHD population. 4,6,10,42 As symptoms of attention-deficit/hyperactivity disorder often become clinically evident at 5-6 years, it could be recommendable to screen at this time-point also in the CHD population to have the possibility to intervene both in the family and in school setting at this early stage.…”

Updates in technology-based interventions for attention deficit hyperactivity disorder

“…Academic tasks were included to promote skills transfer. Their large randomized wait-list control trial involving 172 children aged 6–12 years diagnosed with ADHD found the effect size of the intervention was small, based on blinded clinician ratings [ 65 ▪ ]. A subgroup of the children underwent neuroimaging, which revealed that those with improved behavioural rating tended to exhibit increased functional network reorganization, especially within the salience/ventral attention network [ 66 ].…”

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

“…As discussed in the drowsy driver example, monitoring real-time mental workload and vigilance is of particular importance in safety-critical environments (Lin C. T. et al, 2010 ; Khan and Hong, 2015 ; Aricò et al, 2017 ). Non-invasive BCIs that detect drops in attention level and increased mental fatigue can be utilized in a broad range of operational environments and application domains including aviation (Aricò et al, 2016 ; Hou et al, 2017 ) and industrial workspaces (Schultze-Kraft et al, 2012 ) where safety and efficiency are important, as well as educational and healthcare setups where the system can provide feedback from learners to a teacher (Ko et al, 2017 ; Spüler et al, 2017 ), evaluate sustained attention in e-learning platforms (Chen et al, 2017 ), and execute attention training for clinical patients who suffer from attention deficit hyperactivity disorder (ADHD) (Lim et al, 2019 ). It is even suggested that detection of attention level can be employed in a hybrid BCI system in which an attention classifier is integrated with other BCI algorithms in order to confirm users' focus on the BCI task and validate the produced response, thereby yielding a more reliable and robust performance (Diez et al, 2015 ).…”

“…Multiple algorithms have already been proposed to quantify the level of alertness and mental workload within EEG brain activity. A large number of these models rely on frequency domain features such as theta, alpha and beta band powers, for estimation of attention level and mental fatigue experienced by the user (Lin C. T. et al, 2010 ; Roy et al, 2013 ; Diez et al, 2015 ; Khan and Hong, 2015 ; Aricò et al, 2016 ; Lim et al, 2019 ). On the other hand, some studies have examined non-linear complexity measures of time series EEG signals such as entropy (Liu et al, 2010 ; Min et al, 2017 ; Mu et al, 2017 ), promoting a fast and less costly method for real-time processing.…”