Neurofeedback Training for Autistic Children

Education University of Hong Kong

Status: Invitation-only

Conditions: EEG; Autistic Disorders Spectrum; Neurofeedback; fNIRS; Autism

Treatments: Device: EEG and fNIRS; Device: EEG; Device: fNIRS

Study type: Interventional

Funder types: Other

Identifiers

NCT07149974

ITS/077/22 (Other Grant/Funding Number)

2022-2023-0505

Details and patient eligibility

About

The goal of this study is to learn if a new brain training method, called combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) neurofeedback, can improve thinking, emotions, and social functioning in children with autism spectrum disorder (ASD). It will also learn if this training is practical and safe to use with children in Hong Kong.

The main questions this study aims to answer are:

• Does combined EEG-fNIRS neurofeedback improve attention, emotion regulation, and social skills in children with ASD?
• Is this type of neurofeedback training feasible and well-tolerated by children? Researchers will compare the new combined EEG-fNIRS training with single EEG or fNIRS training to see if it provides additional benefits.

Participants will:

.Receive sessions of EEG-fNIRS neurofeedback training. .Complete assessments of thinking skills, emotional regulation, and social functioning before and after training.

Full description

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition characterized by difficulties in social communication and interaction, often accompanied by cognitive and emotional regulation challenges. In Hong Kong and many other countries, ASD is increasingly prevalent. Despite this, the brain health of autistic individuals has been relatively neglected in both healthcare systems and public policies. There is also a lack of approaches and technologies that directly intervene with brain function. Since many autistic children experience poor vocational and health outcomes in adulthood, there is a strong need to develop effective and accessible neuroscience-based treatments.

This project aims to apply cutting-edge neuroscientific methods to develop an innovative closed-loop brain training intervention for children with ASD. The intervention will combine electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) in a unified neurofeedback training system. Neurofeedback training teaches individuals to self-regulate brain activity by providing real-time feedback. In the traditional neurofeedback study, EEG has been used to guide neurofeedback by monitoring electrical activity in the brain, while more recently fNIRS has been used to track hemodynamic activity. However, no existing neurofeedback system has integrated these two modalities. Combining EEG and fNIRS provides an opportunity to enhance neurovascular coupling, the relationship between neural activity and blood flow, which is often altered in neuropsychiatric conditions such as autism.

The proposed neurofeedback application will include multiple training modules designed to address cognitive, emotional, and social difficulties common in autism. The cognitive training module will target brain activity patterns associated with attention and executive function. The affective training module will focus on modulating frontal brain activity linked to emotional regulation. The social training module will aim to enhance neural and hemodynamic activity associated with social cognition and communication. By integrating both EEG and fNIRS indices, the system will encourage children to regulate electrical and hemodynamic activity simultaneously, which cannot be achieved using either modality alone.

To maximize engagement, the application will incorporate ecologically valid feedback stimuli and reward-based learning principles. Instead of relying solely on abstract indicators such as bars or tones, the feedback will involve intrinsically rewarding stimuli, such as videos or positive visual cues, to increase motivation and adherence. The training difficulty will be adjusted progressively based on individual performance to ensure sustained engagement and improvement.

In addition, the system will be developed as a cross-device application using open-source lab streaming layer (LSL) software, ensuring compatibility with a wide range of EEG and fNIRS devices. The hardware and software will be optimized to ensure high-quality signals, including the use of shielded wet electrodes for EEG to reduce noise and short-separation channels in fNIRS to minimize extracerebral signal contamination. These features will allow neurofeedback training to be conducted with minimal environmental interference, enhancing both reliability and clinical applicability.

Through this proof-of-concept project, this project aims to establish the feasibility of combined EEG-fNIRS neurofeedback as a novel form of brain training for autistic children. If successful, this approach has the potential to offer a comprehensive, technology-based neurorehabilitation solution that can improve functional outcomes, reduce healthcare burdens, and foster innovation in neurotechnology in Hong Kong.

Enrollment: 30 estimated patients

Sex: All

Ages: 8 to 12 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Children aged 8 to 12 years
• Previous diagnosis of autism spectrum disorder (ASD) or Asperger's syndrome
• No intellectual impairment or studying in mainstream schools
• Right-handedness
• Normal or corrected-to-normal vision

Exclusion criteria

- Not meeting any of the above inclusion criteria

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Triple Blind

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30 participants in 3 patient groups

Combined EEG-fNIRS group

Experimental group

Description:

The participants will undertake two essential phases of neurofeedback training: (1) baseline and (2) training. During baseline phase (typically 3 minutes), the user plays the default neurofeedback game to obtain baseline EEG and fNIRS recordings. On the basis of these signals, the mean and standard deviation of the index of interest will be extracted and calculated. During the training phase (typically 5 minutes), the signal processing is almost identical to the one during baseline phase, but the moment-to-moment outcome variable will be Z-normalized according to the mean and SD of the target index. Once the data is pushed to the LSL stream for use in neurofeedback game interaction, participants can see the corresponding changes in the game screen. For the combined EEG-fNIRS group, both the EEG and fNIRS indices will be be extracted. To encourage integration, the lower value of the two will be chosen as the outcome variable.

Treatment:

Device: EEG and fNIRS

EEG group

Experimental group

Description:

The participants will undertake two essential phases of neurofeedback training: (1) baseline and (2) training. During baseline phase (typically 3 minutes), the user plays the default neurofeedback game to obtain baseline EEG and fNIRS recordings. On the basis of these signals, the mean and standard deviation of the index of interest will be extracted and calculated. During the training phase (typically 5 minutes), the signal processing is almost identical to the one during baseline phase, but the moment-to-moment outcome variable will be Z-normalized according to the mean and SD of the target index. Once the data is pushed to the LSL stream for use in neurofeedback game interaction, participants can see the corresponding changes in the game screen. For EEG, the frontal theta/beta ratio, left-right difference in frontal alpha power, and the mu power are chosen as the target training indices.

Treatment:

Device: EEG

fNIRS group

Experimental group

Description:

The participants will undertake two essential phases of neurofeedback training: (1) baseline and (2) training. During baseline phase (typically 3 minutes), the user plays the default neurofeedback game to obtain baseline EEG and fNIRS recordings. On the basis of these signals, the mean and standard deviation of the index of interest will be extracted and calculated. During the training phase (typically 5 minutes), the signal processing is almost identical to the one during baseline phase, but the moment-to-moment outcome variable will be Z-normalized according to the mean and SD of the target index. Once the data is pushed to the LSL stream for use in neurofeedback game interaction, participants can see the corresponding changes in the game screen. For fNIRS, the level of prefrontal activation (HbO or HbR), the left-right difference in prefrontal activation, and the motor cortex activation are chosen as the target training indices, respectively.

Treatment:

Device: fNIRS