EPFL's implantable chip reads brain-network chatter to spot mood states in real time
Neural chip decodes interactions between brain regions
Researchers at EPFL have built LogSync, a 4x2 mm implantable system-on-chip that records activity across multiple brain regions and extracts biomarkers for oscillation strength and cross-regional synchrony. A machine-learning algorithm uses those network-level signals to classify cognitive and emotional states and trigger stimulation on demand. Validated on human psychiatric recordings and in rats, the chip runs on under 200 microwatts, a fraction of what comparable neural implants draw, thanks to co-designed hardware and logarithmic computation.
Our work is based on the growing body of research suggesting that psychiatric disorders arise more from disruptions in communication between brain regions than from dysfunction of any single region.
- ghm2180
I evolved my mental model for adhd by reading newer research like:https://www.economist.com/leaders/2024/10/30/adhd-should-not...
Metaphorically and analogy wise: If you think as genes as the holes on a flute, gene expression as the music then environment is what determines "bad" music. But there is no one specific gene that is known or corellated specifically for adhd symptoms.
ADHD is likely genetically caused but the genes are in everyone just expressed in specific environmental circumstances impossible to know for sure in a lab(cause they mostly look at outward behavior in lab settings and fmri blood flow).
But I've been doing meditation(45mins 3-4 days a week) I've also learnt for myself that 30 mins of power naps does wonders for my mind. The medication's effectiveness AND gated on the former two; SNRI, Dopamine reuptake inhibition is like carpet bombing and only makes you ready for rewiring: you still need to rewire with therapeutic and Behavior Therapy techniques.
But I know for a fact that my brain waves are different on days I do meditation and power naps.
I really do want to know if deep brain neural activity detection data can detect bad waves and possibly close the loop by providing corrective waves.
- AbstractH24
Most of this already exists, and I already have it implanted in my brain
Articles can be weird sometimes in the details they include/omit.
- D-Machine
> The proposed SoC was extensively validated on two intracranial electroencephalography (iEEG) datasets related to psychiatric disorders, achieving 80.9%/74.8% sensitivity and 74.2%/73.1% specificity on the multi-source interference task (MSIT) and the emotional conflict resolution (ECR) task, respectively. We further evaluated the SoC on the public DEAP dataset for electroencephalography (EEG)-based emotion recognition, achieving 70.5%/72.5% sensitivity and 69.8%/66.3% specificity for the arousal and valence states, respectively. [https://ieeexplore.ieee.org/document/11685197]
I dunno, really need to see the comparative performance of other classifiers and the base rates in these datasets to know whether these performance metrics are anything we should care about, and whether e.g. a classifier on simple verbal probes to the subjects might simply yield better predictions than going through the EEG signals. You can do some really interesting EEG biofeedback stuff, so maybe this is useful, but I would think the full EEG head-gear would also provide much richer and more useful information than a measurement based on a single chip. Presumably this would be for psychiatric disorders or brain injuries where the core dysfunction we know is limited to a very specific region(s).
- layer8
The end game is described in this short story: https://gwern.net/doc/fiction/science-fiction/1995-egan.pdf
- HPsquared
This is going to be something in the cookie banners at some point.