Summary: A recent study used EEG and AI to analyze mental responses to psychological stimuli to identify Parkinson’s disease with near-perfect accuracy. Scientists found that Parkinson’s patients approach emotions differently, struggling with recognizing anxiety, contempt, and wonder and focusing more on personal power than polarity.
EEG data from 20 people and 20 healthier controls was analyzed using system learning, achieving an F1 report of 0.97 for clinical accuracy. This breakthrough offers a non-invasive, goal medical process, possibly revolutionizing earlier detection and treatment for Parkinson’s disease.
Essential Information
- Medical Accuracy: EEG-based psychological research achieved a 0.97 F1 report in identifying Parkinson’s.
- Emotion Patterns: Parkinson’s people recognize emotional intimacy better than polarity, typically confusing opposing thoughts.
- AI Integration: High efficiency was achieved when machine learning systems processed EEG data to accurately distinguish patients from controls.
Origin: Intelligent Computing
A joint study project from the University of Canberra and Kuwait College of Science and Technology has discovered the first-ever diagnosis of Parkinson’s disease using near-perfect correctness by analyzing brain actions to psychological stimuli like watching videos or image.
Instead of relying on clinical expertise and patient self-assessments, the findings provide an objective diagnosis of the debilitating movement disorder, potentially enhancing Parkinson’s disease’s treatment options and overall well-being.
The study was published Oct. 17 in , Intelligent Computing, a Science Partner Journal, in an article titled” Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease”.
Their analysis of the emotional brain focuses on the differences between healthy people and Parkinson’s patients, who are typically thought to have poor judgment about how to express emotions.
The team demonstrated that patients and healthy people with F1 scores of 0. 97 or higher can be identified from brain scan readings of emotional responses alone.
Brainwave data alone gives this diagnostic performance a very high degree of accuracy. The F1 score is a metric that combines recall and precision, where 1 is the highest possible value.
The results demonstrate that Parkinson’s patients had specific emotional perception patterns that helped them comprehend emotional arousal more than emotional valence, making them more sensitive to the intensity of those emotions rather than the pleasantness or unpleasantness of those emotions.
The patients were also found to struggle most with recognizing fear, disgust and surprise, or to confuse emotions of opposite valences, such as mistaking sadness for happiness.
The researchers recorded electroencephalography — or EEG — data, measuring electrical brain activity in 20 Parkinson’s patients and 20 healthy controls.
Participants watched images and video clips that were intended to elicit feelings.
Multiple EEG descriptors were used to extract key features from the recording of EEG data before being transformed into visual representations, which were then analyzed using machine learning tools like convolutional neural networks, to identify patterns in how the patients processed emotions differently from the healthy group.
This processing enabled the distinguishing between healthy controls and patients with absolute accuracy.
Specular power vectors and common spatial patterns are two important EEG descriptors. Specular power vectors record the power distribution across various frequency bands, which are thought to have a relationship with emotional states.
Common spatial patterns improve interclass discriminability by maximizing variance for one class while minimizing variance for another, facilitating a more accurate classification of EEG signals.
As the researchers continue refining EEG-based techniques, emotional brain monitoring has the potential to become a widespread clinical tool for Parkinson’s diagnosis.
The study demonstrates the promise of combining neurotechnology, AI and affective computing to provide objective neurological health assessments.
About this Parkinson’s disease, emotion, and AI research news
Author: Xuwen Liu
Source: Intelligent Computing
Contact: Xuwen Liu – Intelligent Computing
Image: The image is credited to Neuroscience News
Original Research: Open access.
” Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease” by Ramanathan Subramanian et al. Intelligent Computing
Abstract
Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease
While Parkinson’s disease ( PD ) is typically characterized by motor disorder, there is also evidence of diminished emotion perception in PD patients.
This study examines the utility of electroencephalography ( EEG ) signals for analyzing the emotional differences between PD and healthy controls ( HCs ) and for automated PD detection.
We investigate dimensional and categorical emotion recognition and PD versus HC classification from multiple descriptors characterizing emotional EEG signals using traditional machine learning and deep learning techniques applied to multiple EEG descriptors.
Our results reveal that PD patients comprehend arousal better than valence and, among emotion categories, fear, disgust, and surprise less accurately, and sadness most accurately.
Mislabeling analyses reveal confounds in PD data regarding opposite-valence emotions. Emotional EEG responses also achieve near-perfect PD versus HC recognition.
Cumulatively, our study demonstrates that ( a ) examining implicit responses alone enables ( i ) discovery of valence-related impairments in PD patients and ( ii ) differentiation of PD from HC and that ( b ) emotional EEG analysis is an ecologically valid, effective, practical, and sustainable tool for PD diagnosis vis-à-vis self-reports, expert assessments, and resting-state analysis.
