EPFL researchers have developed an implantable neural chip, Log Sync, that tracks communication across brain regions in real time. The system identifies cognitive and emotional states by extracting biomarkers from brain wave synchrony. Deep brain stimulation (DBS) is a therapeutic technique used for movement disorders like Parkinson's and is being explored for severe psychiatric illnesses. However, most existing DBS systems operate in an "open loop" manner, delivering stimulation according to predefined settings rather than adapting to brain activity. Even among closed-loop systems, many rely on signals from a single brain region, which is a major limitation for psychiatric disorders where clinically relevant information is distributed across networks of interacting brain regions.
The INL team at EPFL's Geneva campus developed Log Sync, a low-power implantable system-on-chip. It records brain activity and extracts biomarkers describing the strength of brain waves and synchrony between signals from different regions. A machine-learning algorithm analyzes these network-level biomarkers to distinguish cognitive and emotional states and generate stimulation triggers when specific states are detected. The work, published in the IEEE Journal of Solid-State Circuits, was developed as part of a CHF 6.6 million grant from the US National Institute for Mental Health (NIMH).
The researchers evaluated Log Sync using human brain recordings from previous psychiatric experiments. One dataset captured changes in cognitive engagement while performing a mental task, while another captured emotional arousal as participants viewed video clips. The algorithm distinguished these cognitive and emotional states from neural recordings alone. In further experiments conducted in rats at the University of Minnesota, the implant tracked ongoing oscillations and generated stimulation triggers at predefined phases of brain activity. The Log Sync chip measures approximately 4x2 mm. Shoaran says that the major challenge was achieving all this functionality while working within the very limited power budget of an implantable system. Their entire system is co-designed, meaning the algorithms and hardware are developed together with the constraints of an implant in mind. This approach helped achieve network-level neural signal processing and decoding on a single chip, while keeping power consumption under 200 microwatts — a fraction of that typically required by many implantable neural-interface systems. Another innovation is Log Sync's use of logarithmic computations, which simplify the complex operations needed for brain-network analysis and real-time biomarker extraction and decoding.
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