QEEG Brain Mapping
Quantitative EEG analysis and brain mapping: assessment protocols, interpretation, clinical applications, and personalized training plans.
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Showing 6 of 292Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial
Noble, Sandra-Carina, Woods, Eva, Ward, Tomas, Ringwood, John V (2023) · JMIR Research Protocols
Background The number of people with cognitive deficits and diseases, such as stroke, dementia, or attention-deficit/hyperactivity disorder, is rising due to an aging, or in the case of attention-deficit/hyperactivity disorder, a growing population. Neurofeedback training using brain-computer interfaces is emerging as a means of easy-to-use and noninvasive cognitive training and rehabilitation. A novel application of neurofeedback training using a P300-based brain-computer interface has previously shown potential to improve attention in healthy adults. Objective This study aims to accelerate attention training using iterative learning control to optimize the task difficulty in an adaptive P300 speller task. Furthermore, we hope to replicate the results of a previous study using a P300 speller for attention training, as a benchmark comparison. In addition, the effectiveness of personalizing the task difficulty during training will be compared to a nonpersonalized task difficulty adaptation. Methods In this single-blind, parallel, 3-arm randomized controlled trial, 45 healthy adults will be recruited and randomly assigned to the experimental group or 1 of 2 control groups. This study involves a single training session, where participants receive neurofeedback training through a P300 speller task. During this training, the task’s difficulty is progressively increased, which makes it more difficult for the participants to maintain their performance. This encourages the participants to improve their focus. Task difficulty is either adapted based on the participants’ performance (in the experimental group and control group 1) or chosen randomly (in control group 2). Changes in brain patterns before and after training will be analyzed to study the effectiveness of the different approaches. Participants will complete a random dot motion task before and after the training so that any transfer effects of the training to other cognitive tasks can be evaluated. Questionnaires will be used to estimate the participants’ fatigue and compare the perceived workload of the training between groups. Results This study has been approved by the Maynooth University Ethics Committee (BSRESC-2022-2474456) and is registered on ClinicalTrials.gov (NCT05576649). Participant recruitment and data collection began in October 2022, and we expect to publish the results in 2023. Conclusions This study aims to accelerate attention training using iterative learning control in an adaptive P300 speller task, making it a more attractive training option for individuals with cognitive deficits due to its ease of use and speed. The successful replication of the results from the previous study, which used a P300 speller for attention training, would provide further evidence to support the effectiveness of this training tool. Trial Registration ClinicalTrials.gov NCT05576649; https://clinicaltrials.gov/ct2/show/NCT05576649 International Registered Report Identifier (IRRID) DERR1-10.2196/46135
View Full Paper →Application of Artificial Intelligence Techniques for Brain–Computer Interface in Mental Fatigue Detection: A Systematic Review (2011–2022)
Yaacob, Hamwira, Hossain, Farhad, Shari, Sharunizam, Khare, Smith K., Ooi, Chui Ping, Acharya, U. Rajendra (2023) · IEEE Access
Mental fatigue is a psychophysical condition with a significant adverse effect on daily life, compromising both physical and mental wellness. We are experiencing challenges in this fast-changing environment, and mental fatigue problems are becoming more prominent. This demands an urgent need to explore an effective and accurate automated system for timely mental fatigue detection. Therefore, we present a systematic review of brain-computer interface (BCI) studies for mental fatigue detection using artificial intelligent (AI) techniques published in Scopus, IEEE Explore, PubMed and Web of Science (WOS) between 2011 and 2022. The Boolean search expression that comprised (((ELECTROENCEPHALOGRAM) AND (BCI)) AND (FATIGUE CLASSIFICATION)) AND (BRAIN-COMPUTER INTERFACE) has been used to select the articles. Through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, we selected 39 out of 562 articles. Our review identified the research gap in employing BCI for mental fatigue intervention through automated neurofeedback. The AI techniques employed to develop EEG-based mental fatigue detection are also discussed. We have presented comprehensive challenges and future recommendations from the gaps identified in discussions. The future direction includes data fusion, hybrid classification models, availability of public datasets, uncertainty, explainability, and hardware implementation strategies.
View Full Paper →Biofeedback Interventions for Impulsivity-related Processes in Addictive Disorders
Lucas, Ignacio, Solé-Morata, Neus, Baenas, Isabel, Rosinska, Magda, Fernández-Aranda, Fernando, Jiménez-Murcia, Susana (2023) · Current Addiction Reports
Abstract Purpose of Review Biofeedback is a promising technique that has been used as a treatment tool for different psychological disorders. In this regard, central (neurofeedback) and peripheral psychophysiological signals are presented as comprehensible stimuli with the aim of training specific processes. This review summarizes recent evidence about its use for the treatment of impulsivity-related processes in addictive disorders. Recent Findings Neurofeedback (NFB) protocols, based on electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have focused on substance use disorders. Biofeedback protocols using peripheral measures have been mainly based on heart rate variability and focused on behavioral addictions. EEG-NFB reported good results in the reduction of hyperarousal, impulsivity and risk taking in alcohol use disorder, and decreased rates of smoking and less craving in nicotine addiction. In fMRI-NFB, effective NFB performance has been related with better clinical outcomes in substance use disorders; however, its implication for treatment is still unclear. Heart rate variability biofeedback results are scarce, but some interventions have been recently designed aimed at treating behavioral addictions. Summary In addictive disorders, biofeedback interventions for impulsivity-related processes have shown promising results, although the literature is still scarce. Further research should aim at proving the effectiveness of biofeedback protocols as a treatment option for impulsivity in addictive disorders.
View Full Paper →Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study
Voetterl, Helena, van Wingen, Guido, Michelini, Giorgia, Griffiths, Kristi R., Gordon, Evian, DeBeus, Roger, Korgaonkar, Mayuresh S., Loo, Sandra K., Palmer, Donna, Breteler, Rien, Denys, Damiaan, Arnold, L. Eugene, du Jour, Paul, van Ruth, Rosalinde, Jansen, Jeanine, van Dijk, Hanneke, Arns, Martijn (2023) · Biological Psychiatry. Cognitive Neuroscience and Neuroimaging
BACKGROUND: Attention-deficit/hyperactivity disorder is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates. METHODS: The biomarker in this study was developed in a heterogeneous clinical sample (N = 4249) and first applied to two large transfer datasets, a priori stratifying young males (<18 years) with a higher individual alpha peak frequency (iAPF) to methylphenidate (N = 336) and those with a lower iAPF to multimodal neurofeedback complemented with sleep coaching (N = 136). Blinded, out-of-sample validations were conducted in two independent samples. In addition, the association between iAPF and response to guanfacine and atomoxetine was explored. RESULTS: Retrospective stratification in the transfer datasets resulted in a predicted gain in normalized remission of 17% to 30%. Blinded out-of-sample validations for methylphenidate (n = 41) and multimodal neurofeedback (n = 71) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively. CONCLUSIONS: This study introduces a clinically interpretable and actionable biomarker based on the iAPF assessed during resting-state electroencephalography. Our findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.
View Full Paper →ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces
Marcos-Martínez, Diego, Santamaría-Vázquez, Eduardo, Martínez-Cagigal, Víctor, Pérez-Velasco, Sergio, Rodríguez-González, Víctor, Martín-Fernández, Ana, Moreno-Calderón, Selene, Hornero, Roberto (2023) · Computers in Biology and Medicine
BACKGROUND AND OBJECTIVE: Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It is implemented with a closed-loop brain-computer interface (BCI) system that analyzes the user's brain activity in real-time and provides continuous feedback. This paradigm is of great interest due to its potential as a non-pharmacological and non-invasive alternative to treat non-degenerative brain disorders. Nevertheless, currently available NF frameworks have several limitations, such as the lack of a wide variety of real-time analysis metrics or overly simple training scenarios that may negatively affect user performance. To overcome these limitations, this work proposes ITACA: a novel open-source framework for the design, implementation and evaluation of NF training paradigms. METHODS: ITACA is designed to be easy-to-use, flexible and attractive. Specifically, ITACA includes three different gamified training scenarios with a choice of five brain activity metrics as real-time feedback. Among them, novel metrics based on functional connectivity and network theory stand out. It is complemented with five different computerized versions of widespread cognitive assessment tests. To validate the proposed framework, a computational efficiency analysis and an NF training protocol focused on frontal-medial theta modulation were conducted. RESULTS: Efficiency analysis proved that all implemented metrics allow an optimal feedback update rate for conducting NF sessions. Furthermore, conducted NF protocol yielded results that support the use of ITACA in NF research studies. CONCLUSIONS: ITACA implements a wide variety of features for designing, conducting and evaluating NF studies with the goal of helping researchers expand the current state-of-the-art in NF training.
View Full Paper →Mental imagery content is associated with disease severity and specific brain functional connectivity changes in patients with Parkinson's disease
Cherry, Jared, Kamel, Serageldin, Elfil, Mohamed, Aravala, Sai S., Bayoumi, Ahmed, Patel, Amar, Sinha, Rajita, Tinaz, Sule (2023) · Brain Imaging and Behavior
Mental imagery is the mental re-creation of perceptual experiences, events and scenarios, and motor acts. In our previous study, we assessed whether motor imagery (MI) training combined with functional magnetic resonance imaging-based neurofeedback could improve the motor function of nondemented subjects with mild Parkinson's disease (PD) (N = 22). We used visual imagery (VI) (e.g., of scenes or events, but not of self-movements) training without neurofeedback for the control group (N = 22). Notably, both groups showed significant and comparable improvement in motor function after four weeks of daily imagery practice. In this study, we further examined the neural correlates of the motor enhancement as a result of the VI training by analyzing the self-reported VI content during daily practice and relating its quality to the functional connectivity characteristics of the same subjects. We demonstrated that the VI practice encompassed multisensory, spatial, affective, and executive processes all of which are also important for motor function in real life. Subjects with worse global disease severity also showed poorer quality of the VI content. Finally, the quality of the VI content showed significant positive correlations with the functional connectivity changes during the VI tasks in brain areas supporting visuospatial and sensorimotor processes. Our findings suggest that mental imagery training combining VI and MI may enhance motor function in patients with mild PD, and more broadly, underline the importance of incorporating self-reports of thoughts and experiences in neuroimaging studies that examine the brain mechanisms of complex cognitive processes especially in neuropsychiatric patient populations.
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