Neurofeedback Training Protocol Based on Selecting Distinctive Features to Treat or Reduce ADHD Symptoms

Objective. Neurofeedback can reduce ADHD symptoms; however, current programs are relatively long, with fewer concerns about executive function (EF). The present study aimed to investigate a 20-hour combined computerized training neurofeedback program. Methods. Fifty ADHD children were randomly assigned to either the experimental group (EXP) or the wait-list control group (CON), who took training after the post-tests. The EF measures were the Tower of London (ToL), Wisconsin Card Sorting Test (WCST), and Comprehensive Nonverbal Attention Test (CNAT). SNAP-IV and questionnaires reported by parents constituted the behavioral measures. Two-way repeated-measures ANOVA and bootstrapping dependent t-tests were also used. Results. The F-tests revealed the interaction effects on ADHD symptoms and math scores. The EXP had increased the ToL scores, decreased the error and perseverative error rates on WCST, as well as the dysexecutive index on CNAT in the t-test. Conclusions. The training effects were related to behavioral symptoms and functions, EFs, and generalized achievement performances. We suggest that future studies could apply to different patients and examine the maintenance of the program.

Section: The Will Well Neurofeedback (Wwnf) Programmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Effects of Twenty Hours of Neurofeedback-Based Neuropsychotherapy on the Executive Functions and Achievements among ADHD Children

Liao

Guo

et al. 2022

“…16,20–23 Neurofeedback training helps individuals with ADHD modify their brain activity based on a computer game or auditory feedback while their EEG signals are recorded to maintain their EEG signal features within normal range. 16,24,25 Likewise, some studies have revealed the effectiveness of using EMG biofeedback alone or alongside neurofeedback in curing ADHD. 26,27…”

Section: Introductionmentioning

confidence: 99%

“…2 This method for diagnosis of ADHD is not reliable due to the fact that the results of such clinical interviews are dependent on individuals' honesty and perception of psychologists' questions, 11,12 accordingly, several studies have looked for other diagnostic methods. Groups of researchers have investigated the differences between electroencephalogram (EEG) signals of normal individuals and people with ADHD, [13][14][15][16][17][18][19] with regards to the differences associated with the EEG signals of ADHD group and healthy group, neurofeedback training has been incorporated into treating ADHD as a nonpharmaceutical therapeutic method. 16,[20][21][22][23] Neurofeedback training helps individuals with ADHD modify their brain activity based on a computer game or auditory feedback while their EEG signals are recorded to maintain their EEG signal features within normal range.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Investigation of Wavelet Families for Classification of EOG Signals Related to Healthy and ADHD Children

Ayoubipour,

Sho'ouri

2023

Based on previous research, there are differences between eye movements of people with attention-deficit hyperactivity disorder (ADHD) and of healthy people, as a result, the existence of differences regarding the electrooculogram (EOG) signals of the 2 groups exists. Thus, this study aimed to examine the recorded EOG signals of 30 ADHD children and 30 healthy children while performing an attention-related task. For this purpose, the EOG signals of these 2 groups were decomposed utilizing various wavelet functions. Afterward, features, including mean, energy, and standard deviation (SD) of approximation and detail wavelet coefficients were calculated. The Davies–Bouldin (DB) index was used for the evaluation of the feature space quality. Finally, the 2 groups were classified using one-dimensional feature vector and support vector machine (SVM). The SD of detail coefficients (db4) was selected as the most effective feature for separating the 2 groups. Statistical analysis revealed that the values of energy and SD of EOG signals’ detail coefficients were significantly lower in the ADHD group in comparison with the healthy group ( P<.001). These results showed that the speed of the ADHD group's eye movements was slower due to the fact that the high-frequency band activity of EOG signals in the healthy group was higher. In addition, the EOG signals were classified with a detection accuracy of 83.42 ± 3.8%. The results of this study can be applied in designing an EOG biofeedback protocol to treat or mitigate the symptoms of ADHD patients.

“…In addition, brain activity patterns in individuals skilled in a specific field are different from those in non-skilled and novice ones. 18–39 If people learn to change their brain activity to assimilate them to those in skilled people, acquiring a skill or enhancing a performance will be assumed quicker. Therefore, NF is used to enhance performance in individuals.…”

Section: Introductionmentioning

confidence: 99%

Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects Within Target Areas During Neurofeedback Training

Sho’ouri

2022

Self Cite

In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects’ status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects’ status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.