How Eeg Transforms Neurological Evaluations

We’re honoured to share that our research paper has been published in the International Journal of Clinical Pediatric Dentistry, titled: “Electroencephalography-based Assessment of Neural Responses in Typical and Atypical Children during Dental Treatment.” Read the full study here: https://lnkd.in/dyJYHTst In pediatric dental care, what appears to be a routine check-up can become a complex neurological and emotional experience for children with special needs. Recognising this gap, our study used a 24-channel EEG to observe real-time brain activity in both neurotypical and neurodivergent children during common non-invasive dental procedures such as cleaning and fluoride application. We found that neurodivergent children exhibited distinctly higher frontal theta and lower posterior alpha activity at baseline, suggesting heightened neural sensitivity. During treatment, neurotypical children showed only a brief increase in beta activity (associated with alertness and concentration), while neurodivergent children displayed sustained high beta and low gamma waves, signalling prolonged arousal and elevated stress. Additionally, the theta/beta ratio-a known neurophysiological marker of anxiety- was significantly elevated in the neurodivergent group. These children also showed stronger mu rhythm suppression and greater auditory response amplitudes, pointing toward increased tactile and sensory sensitivity. While neurotypical children began to adapt within the first 30% of the session, neurodivergent children demonstrated minimal neural adaptation throughout the procedure, correlating with heightened anxiety and behavioural discomfort. This study highlights the need to move beyond “one size fits all” dental care and toward relationship-based, neuroscience-informed models that centre around empathy. By incorporating tactile supports, sensory-friendly strategies, and preparatory tools, we can create environments that respect and accommodate neurological differences. This isn’t just about improving procedures; it’s about understanding the child behind the behaviour, and designing care that meets both their emotional and neurological needs.

A recent study caught my attention. Researchers showed that longitudinal EEG can detect brain changes linked to Alzheimer’s before symptoms even appear. Not imaging. Not subjective scoring. Brain activity itself. Even more interesting: The signals weren’t just detectable, they were trackable over time and tied to underlying pathology. This raises a bigger question for clinical trials: If we can measure functional brain changes earlier and continuously… why are we still relying so heavily on intermittent, subjective endpoints? We may be underestimating what’s happening between visits, and more importantly what we’re missing. Curious how others are thinking about this shift toward objective, scalable neurophysiology in CNS trials. -----Study link posted in first comment below. #Neuroscience #ClinicalTrials #EEG #Biomarkers #Alzheimers #Neurotech

Could EEG be the key to diagnosing #Alzheimer’s - early, accurately, and affordably? That’s the bold promise explored in “𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐨𝐟 𝐄𝐄𝐆 𝐢𝐧 𝐀𝐥𝐳𝐡𝐞𝐢𝐦𝐞𝐫'𝐬 𝐝𝐢𝐬𝐞𝐚𝐬𝐞 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡” (𝘉𝘳𝘢𝘪𝘯 𝘙𝘦𝘴𝘦𝘢𝘳𝘤𝘩 𝘉𝘶𝘭𝘭𝘦𝘵𝘪𝘯, 2025). And it's a call to rethink how we track one of the most devastating neurodegenerative disorders. While fMRI and PET scans dominate the landscape, this comprehensive review makes a compelling case for EEG as a powerful, underused tool for early and precise detection of Alzheimer’s Disease (AD) - especially when combined with advanced ML and signal processing techniques. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐟𝐨𝐮𝐫 𝐬𝐭𝐚𝐧𝐝𝐨𝐮𝐭 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬: ➤ EEG is fast, portable, and non-invasive—offering real-time brain activity insights at a fraction of the cost of MRI or PET. Translation: It could bring diagnostic power to clinics with fewer resources. ➤ AD-specific EEG patterns include lower signal complexity, diminished synchrony, and slowed activity. These features are now detectable using tools like Multi-Scale Entropy and quantitative EEG (qEEG). ➤ State-of-the-art classifiers (like SVM, CNN, and transformer-based models) have achieved accuracies up to 99.85% on EEG data, significantly outperforming traditional diagnostic methods. 𝐓𝐡𝐞 𝐛𝐢𝐠 𝐜𝐚𝐭𝐜𝐡? 𝐄𝐄𝐆 𝐢𝐬 𝐧𝐨𝐢𝐬𝐲. 𝐌𝐨𝐭𝐢𝐨𝐧 𝐚𝐫𝐭𝐢𝐟𝐚𝐜𝐭𝐬, 𝐛𝐥𝐢𝐧𝐤𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐬𝐰𝐞𝐚𝐭𝐢𝐧𝐠 𝐜𝐚𝐧 𝐝𝐫𝐨𝐰𝐧 𝐨𝐮𝐭 𝐮𝐬𝐞𝐟𝐮𝐥 𝐬𝐢𝐠𝐧𝐚𝐥𝐬. 𝐏𝐥𝐮𝐬, 𝐩𝐚𝐭𝐢𝐞𝐧𝐭 𝐯𝐚𝐫𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 - 𝐚𝐠𝐞, 𝐠𝐞𝐧𝐞𝐭𝐢𝐜𝐬, 𝐜𝐨𝐦𝐨𝐫𝐛𝐢𝐝𝐢𝐭𝐢𝐞𝐬- 𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬 𝐠𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧. BUT: EEG’s low cost and portability make it scalable in real-world settings, from rural clinics to at-home wearables. But to deliver on its promise, we need: 🧪 Better preprocessing pipelines (ICA, adaptive filters) 🧠 Robust, interpretable ML models 🗂️ Larger, open datasets covering the spectrum from MCI to AD to FTD At DeepPsy AG, we’re going a step further. We’re decoding the brain’s electrical language to predict psychiatric treatment response using EEG, not just diagnose disease. That means: ✅ Empowering clinicians with actionable, brain-based diagnostics ✅ Reducing trial-and-error in treatments like SSRIs, rTMS, or ketamine ✅ Bringing precision and compassion to mental health care What if Alzheimer’s detection could be as simple as reading your brain’s electrical fingerprint? 👇 Join us at DeepPsy AG to keep the conversation going. #Neurotech #EEG #Alzheimers #biomarkers #PrecisionPsychiatry #ExplainableAI #BrainHealth #DigitalBiomarkers #DeepPsy

Using EEG for TBI has been limited to rule out potential nonconvulsive status epilepticus. But there's a new reason: secondary brain injury. TBI uses the IMPACT score as the best available predictor of future injury, based on clinical, radiological, and laboratory findings. As good as it is, it doesn't tell the whole story. A recent paper hints at more importance of EEG features compared to IMPACT parameters alone. The combined EEG and IMPACT model revealed that apart from age, the most contributing features to predict poor outcomes were EEG features (see the image). Why is this important? EEG features are the potential sensitivity to secondary injury. Secondary injury is believed to be at least equally important in explaining neurological outcomes compared to primary injury. IMPACT parameters are a reflection of primary injury. So, a combination of measures sensitive to primary (IMPACT) and secondary (EEG) injury may lead to better prediction of outcome. But there's more to it. EEG has a sensitivity to tracking synaptic damage. And there's preclinical and animal work suggests that secondary damage is significantly reflected in synaptic damage. #EEG has started to make its way to assessing TBI patients and more neurologists, non-neurologist physicians, and healthcare providers are using the information gained to better treat over the entire patient journey. #tbi #neurology #pain Tewarie, P. K., Beernink, T. M., Eertman-Meyer, C. J., Cornet, A. D., Beishuizen, A., van Putten, M. J., & Tjepkema-Cloostermans, M. C. (2023). Early EEG monitoring predicts clinical outcome in patients with moderate to severe traumatic brain injury. NeuroImage: Clinical, 37, 103350.

Printed E-Tattoo Ink Reading Brainwaves. UT Austin and UCLA. December 03, 2024 Excerpt: Researchers from UT Austin and UCLA developed a conductive ink that can be printed directly on the surface of a patient's head and measure brainwaves. The e-tattoos serve as sensors for electroencephalography (EEG), that measure the brain's electrical activity. EEG can help diagnose and monitor brain tumors, sleep disorders and other brain issues. "The objective for EEG is a sensor that patients can wear for long periods of time, outside clinical settings without need for constant maintenance," said Nanshu Lu, a pioneer in e-tattoos and professor in UT Cockrell School of Engineering's Department of Aerospace Engineering and Engineering Mechanics, one of the project leaders. The best EEG equipment today includes a cap and rigid or squishy electrodes that attach to the head with long wires. Mapping the patient's head can take several hours and squishy electrodes can dry out quickly. The new method, published in Cell Biomaterials (link enclosed), uses a camera to map the individual head’s shape digitally. Researchers developed an algorithm that designs EEG sensors specifically for the individual and guides a robot printer where to place the conductive ink. Robotic combs are envisioned. Note: The printer does not touch the patient. Ink is propelled fast enough to get through hair. Researchers to date have had success with short-haired patients. In addition to printed sensors and connectors, short cables link the printed e-tattoo to a small, commercial EEG recorder. José del R. Millán, a professor in Cockrell School's Chandra Family Department of Electrical and Computer Engineering and Dell Medical School's Department of Neurology and a leading expert in brain-computer interfaces, is one of the project's co-authors. The e-tattoos could transform brain-computer interfaces, which use the same equipment as EEG, for easy and less obtrusive use in daily activities. Brain-computer interfaces allow people to control devices with their thoughts. This technology can help people with cognitive impairments live better lives. In recent years, Millán has used brain-computer interface technology to develop a brain-powered wheelchair and a mind-propelled decision-making game. "This design is ultra-low-profile and mechanically imperceptible to the user," Millán said. "The device requires less setup and maintenance. The user could eventually wear a hat or helmet to achieve longer recording times of their brain activity.” Two main future goals: 1) Improve the application for patients with longer hair; and 2).Increasing the ink's resistance to friction. Currently, the ink rubs off in the shower or while sleeping. Researchers aim to improve the ink's robustness which would be especially useful for monitoring sleep disorders and unpredictable conditions such as epilepsy. https://lnkd.in/enng2QGp

From Symptoms to Neurofeedback Protocol Treatment starts with a question: Where do we begin? For a neurofeedback practitioner, a new patient's first session often feels like a puzzle with thousands of pieces. A patient, like Max Muster, may describe a constellation of symptoms, from feeling impulsive and restless to struggling with social situations and paying attention. The practitioner's challenge is to translate these subjective experiences into an objective, data-driven treatment plan. This is where IFEN’s new Clinical Hypothesis Report System enters the story. It acts as the initial guide, transforming the patient's symptom self-report into a clear, actionable roadmap for assessment. The report doesn’t offer a diagnosis; rather, it provides a crucial first step—a working hypothesis to test with objective data. The software begins by ranking the patient’s most pressing symptom domains. In Max Muster’s case, Impulsivity & Arousal Regulation tops the list with an average score of 2.5. From there, the report provides a powerful narrative arc, linking these symptoms directly to a specific hypothesis for neurophysiological investigation. For impulsivity, the hypothesis points the practitioner toward specific markers to investigate, such as Frontal Slowing or Beta qEEG markers, the N200/P300 ERP component, and the Salience network. The report even pinpoints the key EEG sites of interest, Fz and Cz, to focus the practitioner’s attention. This guided process continues for other ranked domains, such as Social & Interpersonal Function, which is linked to right-hemisphere Beta and the Salience network. For Attention & Executive Function, the report highlights the Elevated Theta/Beta Ratio (TBR) and the Executive Control Network (ECN) at sites like Fz, Cz, and Pz. Finally, the report provides a visual brain map that highlights the primary areas of interest, bringing the initial hypothesis to life and providing a clear, concise visual guide. The Clinical Hypothesis Report System helps practitioners get an idea of where to begin, ensuring that the journey from symptom to a precise, data-driven treatment plan is as efficient and informed as possible. The final protocol, of course, is then built upon this solid foundation of objective qEEG/ERP data and the practitioner’s comprehensive clinical expertise. See full article at https://lnkd.in/giyQipbu