Emotional Cognition Analysis Enables Near-Perfect Parkinson's Detection

A joint research team from the University of Canberra and Kuwait College of Science and Technology has achieved groundbreaking detection of Parkinson's disease with near-perfect accuracy, simply by analyzing brain responses to emotional situations like watching video clips or images. The findings offer an objective way to diagnose the debilitating movement disorder, instead of relying on clinical expertise and patient self-assessments, potentially enhancing treatment options and overall well-being for those affected by Parkinson's disease. The study was published Oct. 17 in Intelligent Computing, a Science Partner Journal.

Their emotional brain analysis focuses on the difference in implicit emotional reactions between Parkinson's patients, who are generally believed to suffer from impairments in recognizing emotions, and healthy individuals. The team demonstrated they can identify patients and healthy individuals with an F1 score of 0.97 or higher, based solely on brain scan readings of emotional responses. This diagnostic performance edges very close to 100% accuracy from brainwave data alone. The F1 score is a metric that combines precision and recall, where 1 is the best possible value.

The results show that Parkinson's patients displayed specific emotional perception patterns, comprehending emotional arousal better than emotional valence, which means they are more attuned to the intensity of 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 video clips and images designed to trigger emotional responses. After the recording of EEG data, multiple EEG descriptors were processed to extract key features and these were transformed into visual representations, which were then analyzed using machine learning frameworks such as convolutional neural networks, for automatic detection of distinct patterns in how the patients processed emotions compared to the healthy group. This processing enabled the highly accurate differentiation between patients and healthy controls.

Key EEG descriptors used include spectral power vectors and common spatial patterns. Spectral power vectors capture the power distribution across various frequency bands, which are known to correlate with emotional states. Common spatial patterns enhance interclass discriminability by maximizing variance for one class while minimizing it for another, allowing for better 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.

Ravikiran Parameshwara, Soujanya Narayana, Murugappan Murugappan, Ibrahim Radwan, Roland Goecke, Ramanathan Subramanian.
Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson's Disease.
Intell Comput. 2024;3:0084. doi: 10.34133/icomputing.0084

Most Popular Now

AI-Powered CRISPR could Lead to Faster G…

Stanford Medicine researchers have developed an artificial intelligence (AI) tool to help scientists better plan gene-editing experiments. The technology, CRISPR-GPT, acts as a gene-editing “copilot” supported by AI to help...

Groundbreaking AI Aims to Speed Lifesavi…

To solve a problem, we have to see it clearly. Whether it’s an infection by a novel virus or memory-stealing plaques forming in the brains of Alzheimer’s patients, visualizing disease processes...

AI Spots Hidden Signs of Depression in S…

Depression is one of the most common mental health challenges, but its early signs are often overlooked. It is often linked to reduced facial expressivity. However, whether mild depression or...

AI Model Forecasts Disease Risk Decades …

Imagine a future where your medical history could help predict what health conditions you might face in the next two decades. Researchers have developed a generative AI model that uses...

AI Tools Help Predict Severe Asthma Risk…

Mayo Clinic researchers have developed artificial intelligence (AI) tools that help identify which children with asthma face the highest risk of serious asthma exacerbation and acute respiratory infections. The study...

AI Model Indicates Four out of Ten Breas…

A project at Lund University in Sweden has trained an AI model to identify breast cancer patients who could be spared from axillary surgery. The model analyses previously unutilised information...

Smart Device Uses AI and Bioelectronics …

As a wound heals, it goes through several stages: clotting to stop bleeding, immune system response, scabbing, and scarring. A wearable device called "a-Heal," designed by engineers at the University...

AI Distinguishes Glioblastoma from Look-…

A Harvard Medical School–led research team has developed an AI tool that can reliably tell apart two look-alike cancers found in the brain but with different origins, behaviors, and treatments. The...

ChatGPT 4o Therapeutic Chatbot 'Ama…

One of the first randomized controlled trials assessing the effectiveness of a large language model (LLM) chatbot 'Amanda' for relationship support shows that a single session of chatbot therapy...

Overcoming the AI Applicability Crisis a…

Opinion Article by Harry Lykostratis, Chief Executive, Open Medical. The government’s 10 Year Health Plan makes a lot of the potential of AI-software to support clinical decision making, improve productivity, and...

Dartford and Gravesham Implements Clinis…

Dartford and Gravesham NHS Trust has taken a significant step towards a more digital future by rolling out electronic test ordering using Clinisys ICE. The trust deployed the order communications...