A Brain Computer Interface (BCI) for detection of stress in individuals with Autism
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects ~2% of the children in the USA and ~ 0.5% worldwide, and is associated with intellectual and communication disabilities, which are often accompanied by impaired emotional regulation (ER). Electroencephalography (EEG)-based neurofeedback has been successfully used to reduce ASD symptoms. Wearable Sensing’s dry electrode headsets’ comfort and ease of use has facilitated research and neurofeedback practice on children with autism, whose hypersensitivities often cause them to refuse conventional wet electrode EEG systems.
In this webinar, Dr. Murat Akcakaya, associate professor at the University of Pittsburgh, will describe his team’s recently published (1) efforts to develop a BCI that performs real-time interventions to ER based on EEG signatures. Using EEG collected with Wearable Sensing’s DSI-24, Dr. Akcakaya’s BCI was able to reliably differentiate between distress and non-distress conditions in 21 individuals with ASD on a single trial basis during a game with deception. This BCI could ultimately provide real-time, automated feedback that can help guide ER control strategies during current clinical behavioral therapy or in virtual reality scenarios.
0:00 Introduction
9:50 Vision
13:30 Participants & Experimental Setup
15:40 Affective Posner Task
19:30 EEG Preprocessing
21:25 EEG Feature Extraction
22:27 Feature Selection & Classification
23:08 Results
26:34 Conclusions
27:49 Future Work
29:33 Q&A
To request a product demo or more information, contact:
sales@wearablesensing.com
or
1-858-412-1812
Thank you for your interest in Wearable Sensing's technology.
wearablesensing.com
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects ~2% of the children in the USA and ~ 0.5% worldwide, and is associated with intellectual and communication disabilities, which are often accompanied by impaired emotional regulation (ER). Electroencephalography (EEG)-based neurofeedback has been successfully used to reduce ASD symptoms. Wearable Sensing’s dry electrode headsets’ comfort and ease of use has facilitated research and neurofeedback practice on children with autism, whose hypersensitivities often cause them to refuse conventional wet electrode EEG systems.
In this webinar, Dr. Murat Akcakaya, associate professor at the University of Pittsburgh, will describe his team’s recently published (1) efforts to develop a BCI that performs real-time interventions to ER based on EEG signatures. Using EEG collected with Wearable Sensing’s DSI-24, Dr. Akcakaya’s BCI was able to reliably differentiate between distress and non-distress conditions in 21 individuals with ASD on a single trial basis during a game with deception. This BCI could ultimately provide real-time, automated feedback that can help guide ER control strategies during current clinical behavioral therapy or in virtual reality scenarios.
0:00 Introduction
9:50 Vision
13:30 Participants & Experimental Setup
15:40 Affective Posner Task
19:30 EEG Preprocessing
21:25 EEG Feature Extraction
22:27 Feature Selection & Classification
23:08 Results
26:34 Conclusions
27:49 Future Work
29:33 Q&A
To request a product demo or more information, contact:
sales@wearablesensing.com
or
1-858-412-1812
Thank you for your interest in Wearable Sensing’s technology.
wearablesensing.com
