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

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

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.

Variations in Theta/Beta Ratio and Cognitive Performance in Subpopulations of Subjects with ADHD Symptoms: Towards Neuropsychological Profiling for Patient Subgrouping

ADHD is a neurodevelopmental disorder appearing in childhood but remaining in many cases in adults. There are both pharmacological and non-pharmacological approaches to treating ADHD, but they do not have the same efficacy in all subjects. Better knowledge of the neurophysiological basis of this disorder will allow for the design of more effective treatments. Studies performing qEEG analysis in children suggest the existence of subgroups of ADHD patients with different neurophysiological traits. There are fewer studies in adults, who might have undergone plastic changes allowing them to cope with ADHD symptoms along with brain maturation. Herein, we study cognitive performance and the theta/beta ratio in young adults with ADHD symptoms. We found that subjects with ADHD symptoms and low working memory performance (n = 30) present higher theta/beta ratios than controls (n = 40) at O2 and T6 in the eyes-closed condition, as well as a tendency toward a higher theta/beta ratio at O1 and Cz. Subjects with ADHD and high working memory performance (n = 50) do not differ from the controls in their theta/beta ratios at any derivation. Our results suggest that neuropsychological profiling could be useful for patient subgrouping. Further research will allow for the distinction of neuropsychological profiles and their neurophysiological correlates, leading to a better classification of ADHD subtypes, thus improving treatment selection.

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

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.

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

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.

Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension

Background:

Previous research has shown that eye movements are different in patients with attention deficit hyperactivity disorder (ADHD) and healthy people. As a result, electrooculogram (EOG) signals may also differ between the two groups. Therefore, the aim of this study was to investigate the recorded EOG signals of 30 ADHD children and 30 healthy children (control group) while performing an attention-related task.

Methods:

Two features of approximate entropy (ApEn) and Petrosian's fractal dimension (Pet's FD) of EOG signals were calculated for the two groups. Then, the two groups were classified using the vector derived from two features and two support vector machine (SVM) and neural gas (NG) classifiers.

Results:

Statistical analysis showed that the values of both features were significantly lower in the ADHD group compared to the control group. Moreover, the SVM classifier (accuracy: 84.6% ± 4.4%, sensitivity: 85.2% ± 4.9%, specificity: 78.8% ± 6.5%) was more successful in separating the two groups than the NG (78.1% ± 1.1%, sensitivity: 80.1% ± 6.2%, specificity: 72.2% ± 9.2%).

Conclusion:

The decrease in ApEn and Pet's FD values in the EOG signals of the ADHD group showed that their eye movements were slower than the control group and this difference was due to their attention deficit. The results of this study can be used to design an EOG biofeedback training course to reduce the symptoms of ADHD patients.

A new neurofeedback training method based on feature space clustering to control EEG features within target clusters

BACKGROUND

Within the most commonly used neurofeedback training methods, a threshold has been defined for each EEG feature wherein subjects' status during training can be assessed according to the given value. In the present study, a neurofeedback training method based on feature-space clustering was proposed in order to assess subjects' status more accurately.

NEW METHOD

Neural gas algorithm was employed for feature space clustering. Then, the clusters were labeled as initial clusters (where the EEG features were placed prior to training) and target (where the EEG features should be shifted towards during training) ones. A scoring index was defined whose value was determined according to subjects' brain activity. This method was simulated in two versions: soft-boundary and hard-boundary based methods.

RESULTS

The results of the present simulation showed that the proposed hard-boundary based version could guide the subjects towards the boundaries of the target clusters and even their status would be stabilized in case of too many changes in subjects' EEG features. In the proposed soft-boundary based version, in case of too many changes in training features, the subjects would not be encouraged and they could be guided towards the target boundaries.

CONCLUSION

The proposed hard-boundary based version could be effective in guiding a subject towards being placed within the boundaries of target clusters and even beyond them if no specific limits exited for EEG features. As well, the soft-boundary based version could be useful when controlling EEG features within a limit.