Brain Exercising Games With Consumer-Grade Single-Channel Electroencephalogram Neurofeedback: Pre-Post Intervention Study.

BACKGROUND: The aging population is one of the major challenges affecting societies worldwide. As the proportion of older people grows dramatically, so does the number of age-related illnesses such as dementia-related illnesses. Preventive care should be emphasized as an effective tool to combat and manage this situation. OBJECTIVE: The aim of this pilot project was to study the benefits of using neurofeedback-based brain training games for enhancing cognitive performance in the elderly population. In particular, aiming for practicality, the training games were designed to operate with a low-cost consumer-grade single-channel electroencephalogram (EEG) headset that should make the service scalable and more accessible for wider adoption such as for home use. METHODS: Our training system, which consisted of five brain exercise games using neurofeedback, was serviced at 5 hospitals in Thailand. Participants were screened for cognitive levels using the Thai Mental State Examination and Montreal Cognitive Assessment. Those who passed the criteria were further assessed with the Cambridge Neuropsychological Test Automated Battery (CANTAB) computerized cognitive assessment battery. The physiological state of the brain was also assessed using 16-channel EEG. After 20 sessions of training, cognitive performance and EEG were assessed again to compare pretraining and posttraining results. RESULTS: Thirty-five participants completed the training. CANTAB results showed positive and significant effects in the visual memory (delayed matching to sample [percent correct] P=.04), attention (median latency P=.009), and visual recognition (spatial working memory [between errors] P=.03) domains. EEG also showed improvement in upper alpha activity in a resting state (open-eyed) measured from the occipital area (P=.04), which similarly indicated improvement in the cognitive domain (attention). CONCLUSIONS: Outcomes of this study show the potential use of practical neurofeedback-based training games for brain exercise to enhance cognitive performance in the elderly population.

A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment.

INTRODUCTION: This study examines the clinical efficacy of a game-based neurofeedback training (NFT) system to enhance cognitive performance in patients with amnestic mild cognitive impairment (aMCI) and healthy elderly subjects. The NFT system includes five games designed to improve attention span and cognitive performance. The system estimates attention levels by investigating the power spectrum of Beta and Alpha bands. METHODS: We recruited 65 women with aMCI and 54 healthy elderly women. All participants were treated with care as usual (CAU); 58 were treated with CAU + NFT (20 sessions of 30 minutes each, 2-3 sessions per week), 36 with CAU + exergame-based training, while 25 patients had only CAU. Cognitive functions were assessed using the Cambridge Neuropsychological Test Automated Battery both before and after treatment. RESULTS: NFT significantly improved rapid visual processing and spatial working memory (SWM), including strategy, when compared with exergame training and no active treatment. aMCI was characterized by impairments in SWM (including strategy), pattern recognition memory, and delayed matching to samples. CONCLUSION: In conclusion, treatment with NFT improves sustained attention and SWM. Nevertheless, NFT had no significant effect on pattern recognition memory and short-term visual memory, which are the other hallmarks of aMCI. The NFT system used here may selectively improve sustained attention, strategy, and executive functions, but not other cognitive impairments, which characterize aMCI in women.

Effectiveness of neurofeedback versus medication for attention-deficit/hyperactivity disorder.

BACKGROUND: Neurofeedback (NF) is an operant conditioning procedure that trains participants to self-regulate brain activity. NF is a promising treatment for attention-deficit/hyperactivity disorder (ADHD), but there have been only a few randomized controlled trials comparing the effectiveness of NF with medication with various NF protocols. The aim of this study was therefore to evaluate the effectiveness of unipolar electrode NF using theta/beta protocol compared with methylphenidate (MPH) for ADHD. METHODS: Children with newly diagnosed ADHD were randomly organized into NF and MPH groups. The NF group received 30 sessions of NF. Children in the MPH group were prescribed MPH for 12 weeks. Vanderbilt ADHD rating scales were completed by parents and teachers to evaluate ADHD symptoms before and after treatment. Student's t-test and Cohen's d were used to compare symptoms between groups and evaluate the effect size (ES) of each treatment, respectively. RESULTS: Forty children participated in the study. No differences in ADHD baseline symptoms were found between groups. After treatment, teachers reported significantly lower ADHD symptoms in the MPH group (P = 0.01), but there were no differences between groups on parent report (P = 0.55). MPH had a large ES (Cohen's d, 1.30-1.69), while NF had a moderate ES (Cohen's d, 0.49-0.68) for treatment of ADHD symptoms. CONCLUSION: Neurofeedback is a promising alternative treatment for ADHD in children who do not respond to or experience significant adverse effects from ADHD medication.

EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation.

Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.