via Stable Diffusion
Research team led by Professor Sanghyun Park of DGIST has contributed to realizing AI medical services by developing a brain wave classification deep-learning model
– Research team led by Professor Sanghyun Park at DGIST developed a deep-learning technology capable of accurately classifying the brain waves of a subject using only a small amount of data
– Contributes significantly to improving the utilization of deep-learning models, which require a large amount of data from subjects to classify brain waves
– Research findings have been published in IEEE Transactions on Neural Networks and Learning Systems, a world-renowned academic journal in the AI field
The research team led by Professor Sanghyun Park from the Department of Robotics and Mechanical Engineering at DGIST (President Kuk Yang) announced that they have developed a few-shot learning model capable of accurately classifying brain waves using a small amount of information. A large amount of brain wave data collected from target subjects is needed for classifying new brain waves using existing deep-learning models; however, the newly developed deep-learning model is capable of accurately classifying brain waves even with a small amount of data, which may contribute to future research related to brain waves.
Brain wave data vary significantly by person. The distribution of brain waves differs by performer even when the same task is performed; thus, most existing classification models collect data from performers and label them to be used for training, focusing only on intra-subject classification. Therefore, the brain waves of a person who did not participate in the training could not be classified using those classification models.
To overcome this drawback, research has been actively conducted on “domain adaptation” models in which deep-learning models are used to infer the brain wave signals of a target subject; however, the problem remains where the models cannot be easily applied to new subjects because they need to learn the brain wave data of those subjects as well. Furthermore, other studies are being conducted on the optimization of transfer learning models where brain wave data collected from multiple individuals are trained, but their usability is rather low since a large amount of brain wave data is still required.
The research team led by Professor Park thus developed a new deep-learning model capable of accurately classifying brain waves according to the brain wave characteristics of each subject when the ground truth of a small amount of data is given from the brain wave data obtained from target subjects. For effectively learning the relation between a small amount of data and the remaining brain waves, meaningful features are first extracted from the brain wave data by using the embedding module, and then the temporal attention module is used to highlight important features from the extracted features while reducing unnecessary noise.
Subsequently, the aggregation attention module is used to find only important data from the given brain wave data to identify the features of the target subject’s intention as represented in the brain waves. Lastly, the relation module is used to calculate the relation between brain wave features and vectors. Also, brain wave classification fine-tuning technology was developed to ensure that brain waves are accurately classified through optimization.
The deep-learning model newly developed by the research team exhibited up to 76% classification accuracy for the intention of a target subject using 20 brain wave data points in inter-subject classification. Considering that the accuracy of previously proposed methods (intra-subject classification, transfer learning, and other few-shot learning methods) is 64–73%, the newly developed model demonstrated superior performance.
Professor Park said, “The brain wave classification deep-learning model developed in this study is capable of accurately classifying brain wave with only a small amount of information, without having to newly build learning data from subjects, and therefore, it is expected to contribute to other related research on brain waves requiring individualization.” He further added, “Our technology will be further enhanced to be more universally utilized in various biosignal analyses.”
This research was supported by the “Intelligent big data integrated platform development project for customized health management services of police officers” of the Korean National Police Agency and the “Commercialization project of immersive human robot multi-sensory exchange technology” of DGIST. The findings were published in IEEE Transactions on Neural Networks and Learning Systems, which is a world-renowned academic journal in the AI field.
Original Article: A brain wave classification deep-learning model
The Latest Updates from Bing News
Go deeper with Bing News on:
Brain wave data
- Brain Wave and Skin Response Sensors Elevate Consumer Insights
Fresneda found that if you add electroencephalograms (EEG) probes, which detect brain waves, and galvanic skin response (GSR ... he said it’s potentially capable of collecting GSR data from smart ...
- Marketing experts measure brain waves and skin current to predict emotions
Machines still can't think, but now they can validate your feelings, based on new research from New Jersey Institute of Technology Assistant Professor Jorge Fresneda.
- brAIn_waves Primary: Episode 1 looks at data and algorithms
brAIn_waves series for Primary Schools. In the first episode of this two-part series, we will be learning about two of the fundamental building blocks of Artificial Intelligence (AI) - data and ...
- How Sleep Engineering Could Help Heal the Brain
Stimulating the sleeping brain may ease suffering from memory loss, stroke or mental health problems I t was late, and Sonia was alone in an unfamiliar town, trying to find her way home. The map ...
- Understanding AI: The brAIn_waves Primary series is here to help!
The Dream Space TV: brAIn_waves Primary School series is a collaboration between RTÉ Learn and Microsoft Dream Space.
Go deeper with Bing News on:
Deep learning model
- AI deep learning model diagnoses symptoms of joint diseases early and with high accuracy, say researchers
Scientists say they have developed an artificial intelligence deep learning model with the ability to detect the early signs of degenerative joint diseases with a high degree of accuracy.
- Anything-in anything-out: A new modular AI model
Researchers at EPFL have developed a new, uniquely modular machine learning model for flexible decision-making. It is able to input any mode of text, video, image, sound, and time-series and then ...
- Accelerating the discovery of single-molecule magnets with deep learning
Single-molecule magnets (SMMs) are exciting materials. In a recent breakthrough, researchers have used deep learning to predict SMMs from 20,000 metal complexes. The predictions were made solely based ...
- A novel deep learning modeling approach guided by mesoscience
Deep learning modeling that incorporates physical knowledge is currently a hot topic, and a number of excellent techniques have emerged. The most well-known one is the physics-informed neural networks ...
- Deep Learning Model Accurately Detects, Predicts Alzheimer’s Disease
Novel deep learning model leverages metabolic biomarkers to predict the likelihood that Alzheimer's disease will develop long before clinical symptom onset.