Is your EEG "brain test" actually measuring blinks and muscle twitches instead of your brain? A new study from Roche (Denis A. Engemann) addressed a quiet issue in the EEG + machine learning world: Many models perform well, 𝐛𝐮𝐭 𝐨𝐧𝐥𝐲 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞𝐲’𝐫𝐞 𝐫𝐞𝐥𝐲𝐢𝐧𝐠 𝐨𝐧 𝐧𝐨𝐧-𝐛𝐫𝐚𝐢𝐧 𝐬𝐢𝐠𝐧𝐚𝐥𝐬, 𝐥𝐢𝐤𝐞 𝐞𝐲𝐞 𝐦𝐨𝐯𝐞𝐦𝐞𝐧𝐭𝐬, 𝐟𝐚𝐜𝐢𝐚𝐥 𝐭𝐞𝐧𝐬𝐢𝐨𝐧, 𝐨𝐫 𝐡𝐞𝐚𝐫𝐭𝐛𝐞𝐚𝐭 𝐚𝐫𝐭𝐞𝐟𝐚𝐜𝐭𝐬. 𝑨𝒏𝒅 𝒘𝒉𝒆𝒏 𝒕𝒉𝒆𝒔𝒆 𝒂𝒓𝒕𝒆𝒇𝒂𝒄𝒕𝒔 𝒂𝒓𝒆 𝒓𝒆𝒎𝒐𝒗𝒆𝒅? 𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆 𝒅𝒓𝒐𝒑𝒔. Meaning: what we thought was a brain biomarker might be something else entirely. ✅ 𝐁𝐨𝐝𝐲 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐜𝐚𝐧 𝐦𝐢𝐬𝐥𝐞𝐚𝐝 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐬: EEG measures electricity from your head, but not all signals come from your brain. Eye blinks, jaw clenches, and even heartbeats can fool AI models, giving the illusion of accuracy. ✅ 𝐂𝐥𝐞𝐚𝐧 𝐄𝐄𝐆 𝐝𝐚𝐭𝐚 𝐦𝐚𝐤𝐞𝐬 𝐚 𝐛𝐢𝐠 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞: The researchers found that when these non-brain signals were fully removed, many models became less accurate. This showed how much some models depended on body signals instead of brain waves. ✅ 𝐍𝐞𝐰 𝐦𝐞𝐭𝐡𝐨𝐝𝐬 𝐜𝐚𝐧 𝐟𝐢𝐱 𝐭𝐡𝐢𝐬: Using advanced techniques (Morlet wavelets and special mathematical models), the study showed we can make EEG biomarkers clearer and more accurate, based on true brain signals. 𝘍𝘰𝘳 𝘌𝘌𝘎 𝘵𝘰 𝘵𝘳𝘶𝘭𝘺 𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮 𝘮𝘦𝘯𝘵𝘢𝘭 𝘩𝘦𝘢𝘭𝘵𝘩𝘤𝘢𝘳𝘦, 𝘸𝘦 𝘮𝘶𝘴𝘵 𝘣𝘦 𝘴𝘶𝘳𝘦 𝘸𝘦’𝘳𝘦 𝘮𝘦𝘢𝘴𝘶𝘳𝘪𝘯𝘨 𝘸𝘩𝘢𝘵 𝘳𝘦𝘢𝘭𝘭𝘺 𝘮𝘢𝘵𝘵𝘦𝘳𝘴: 𝘵𝘩𝘦 𝘣𝘳𝘢𝘪𝘯 𝘪𝘵𝘴𝘦𝘭𝘧. That’s why, at DeepPsy AG, we’re obsessed with making sure our biomarkers truly reflect brain function. Not noise. Not guesses. Actual biology. At DeepPsy AG, this #precision is our mission. 🧠 We build EEG tests using explainable AI to accurately identify which treatments (like antidepressants, magnetic stimulation, ketamine, or electroconvulsive therapy) will help specific patients, based on their actual brain signals. ✅ For clinicians, it means making better treatment choices: without guessing. ✅ For patients, it means quicker relief and fewer failed treatments. 👇 Want EEG biomarkers you can trust? Let’s connect. 👇 Let’s also 𝐜𝐨𝐧𝐧𝐞𝐜𝐭 𝐢𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐢𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐩𝐬𝐲𝐜𝐡𝐢𝐚𝐭𝐫𝐲, 𝐄𝐄𝐆, 𝐨𝐫 𝐧𝐞𝐮𝐫𝐨-𝐀𝐈. #EEG #Biomarkers #psychiatry #prediction #health #precision #AI
EEG’s ability to monitor treatment response over time is a game-changer for personalized medicine. Tracking shifts in brainwave patterns could help clinicians fine-tune interventions. However, this is possible only if we address the current variability in EEG interpretations and artifact contamination.
EEG stands out for being non-invasive, cost-effective, and fast, especially compared to fMRI or PET. But its low spatial resolution and susceptibility to noise remain hurdles. Tackling these challenges is key to unlocking its full potential as a biomarker tool in complex neurological and psychiatric conditions.
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3moEEG biomarkers could revolutionize how we monitor and understand brain disorders. But without consistent protocols and robust datasets, the risk of bias and variability remains too high for broad clinical use.