L’intéraction entre la biologie et l’électronique est une discpline en pleine essort. De nom-breux systèmes électroniques tentent de s’interconnecter avec des tissus ou des cellules vivantesafin de décoder l’information biologique. Le Potentiel d’action (PA) est au coeur de codagebiologique et par conséquent il est nécéssaire de pouvoir les repérer sur tout type de signal bio-logique. Par conséquent, nous étudions dans ce manuscrit la possibilité de concevoir un circuitélectronique couplé à un système de microélectrodes capable d’effectuer une acquisition, unedétection des PAs et un enregistrement des signaux biologiques. Que ce soit en milieu bruitéou non, nous considérons le taux de détection de PA et la contrainte de temps réel commedes notions primordiales et la consommation en silicium comme un prix à payer. Initialementdéveloppés pour l’étude de signaux neuronaux et pancréatiques, ces systèmes conviennent par-faitement pour d’autres type de cellules. / Interaction between biology and electronic is in expansion. Many electronic systems aretrying to interconnect with tissues or living cells to decode biological information. The ActionPotential (AP) is the heart of biological coding and therefore it is necessary to be able to locateit from any type of biological signal. Therefore, we study in this manuscript the possibility ofdesigning an electronic circuit coupled to microelectrodes capable of acquisition, detection ofPAs and recording of biological signals. Whether or not in a noisy environment, we consider thedetection rate of PA and the real time-computing constraint as an hard specificationand andsilicon area as a price to pay. Initially developed for the study of neural signals and pancreatic,these systems are ideal for other types of cells.

Epilepsy affects more than 50 million people and is one of the most prevalent neurological disorders and has a high impact on the quality of life of those suffering from it. However, 70% of epilepsy patients can live seizure free with proper diagnosis and treatment. Patients are evaluated using scalp EEG recordings which is cheap and non-invasive. Diagnostic yield is however low and qualified personnel need to process large amounts of data in order to accurately assess patients. MindReader is an unsupervised classifier which detects spectral anomalies and generates a hypothesis of the underlying patient state over time. The aim is to highlight abnormal, potentially epileptiform states, which could expedite analysis of patients and let qualified personnel attest the results. It was used to evaluate 95 scalp EEG recordings from healthy adults and adult patients with epilepsy. Interictal Epileptiform discharges (IED) occurring in the samples had been retroactively annotated, along with the patient state and maneuvers performed by personnel, to enable characterization of the classifier’s detection performance. The performance was slightly worse than previous benchmarks on pediatric scalp EEG recordings, with a 7% and 33% drop in specificity and sensitivity, respectively. Electrode positioning and partial spatial extent of events saw notable impact on performance. However, no correlation between annotated disturbances and reduction in performance could be found. Additional explorative analysis was performed on serialized intermediate data to evaluate the analysis design. Hyperparameters and electrode montage options were exposed to optimize for the average Mathew’s correlation coefficient (MCC) per electrode per patient, on a subset of the patients with epilepsy. An increased window length and lowered amount of training along with an common average montage proved most successful. The Euclidean distance of cumulative spectra (ECS), a metric suitable for spectral analysis, and homologous L2 and L1 loss function were implemented, of which the ECS further improved the average performance for all samples. Four additional analyses, featuring new time-frequency transforms and multichannel convolutional autoencoders were evaluated and an analysis using the continuous wavelet transform (CWT) and a convolutional autoencoder (CNN) performed the best, with an average MCC score of 0.19 and 56.9% sensitivity with approximately 13.9 false positives per minute.

In neural implants and biohybrid research systems, the integration of electrode recording and stimulation front-ends with pre-processing circuitry promises a drastic increase in real-time capabilities [1,6]. In our proposed neural recording system, constant sampling with a bandwidth of 9.8kHz yields 6.73μV input-referred noise (IRN) at a power-per-channel of 0.34μW for the time-continuous ΔΣ−modulator, and 0.52μW for the digital filters and spike detectors. We introduce dynamic current/bandwidth selection at the ΔΣ and digital filter to reduce recording bandwidth at the absence of spikes (i.e. local field potentials). This is controlled by a two-level spike detection and adjusted by adaptive threshold estimation (ATE). Dynamic bandwidth selection reduces power by 53.7%, increasing the available channel count at a low heat dissipation. Adaptive back-gate voltage tuning (ABGVT) compensates for PVT variation in subthreshold circuits. This allows 1.8V input/output (IO) devices to operate at 0.4V supply voltage robustly. The proposed 64-channel neural recording system moreover includes a 16-channel adaptive compression engine (ACE) and an 8-channel on-chip current stimulator at 3.3V. The stimulator supports field-shaping approaches, promising increased selectivity in future research.