Auto-adaptive neural decoder for clinical brain-spine interfacing
CEA/LETI/CLINATEC invite applications for postdoctoral position to work on the HORIZON-EIC project. The project goal is to explore novel solutions for functional rehabilitation and/or compensation for people with sever motor disabilities using auto-adaptive Brain-Machine Interface (BMI) / neuroprosthetics. Neuroprosthetics record, and decode brain neuronal signal for activating effectors (exoskeleton, implantable spinal cord stimulator etc.) directly without physiological neural control command pass way interrupted by spinal cord injury. A set of algorithms to decode neuronal activity recorded at the level of the cerebral cortex (Electrocorticogram) using chronic WIMAGINE implants were developed at CLINATEC and tested in the frame of 2 clinical research protocols in tetraplegics in Grenoble and in paraplegics in Lausanne. The postdoctoral fellow will contribute to the next highly ambitious scientific breakthroughs addressing the medical needs of patients. The crucial improvement of usability may be achieved by alleviating the need of constant BMI decoder recalibration introducing an auto-adaptive framework to train the decoder in an adaptive manner during the neuroprosthetics self-directed use. Auto-adaptive BMI (A-BMI) adds a supplementary loop evaluating from neuronal data the level of coherence between user’s intended motions and effector actions. It may provide BMI task information (labels) to the data registered during the neuroprosthetics self-directed use to be employed for BMI decoder real-time update. Innovative A-BMI neural decoder will be explored and tested offline and in real-time in ongoing clinical trials.
Postdoctoral fellow in AI, real time signal processing and software for real time epilepsy prediction/forecasting for closed loop neuromodulation by focal Cooling.
To date seizure suppression stimulation technologies (electrical stimulation) are majorly based on seizure detection procedure. No study has provided sound evidence that prospective seizure prediction/forecasting can be used to trigger closed loop therapeutics for drug resistant epilepsy treatment. Our proposal is based on the existing motor brain-computer interface algorithms already in clinical use. They can be adapted to generate prediction/forecasting of seizures occurrence. Our working hypothesis is that treating during high-risk seizures periods and not during the actual seizure would require relatively minor doses of the therapeutical element. This will reduce the power consumption and open the door to fully implantable system. Decoding algorithms will be potentially redesigned to respond better to the epileptic seizures forecasting task. They will be compared to the state of the art CNN based approaches, and other approaches. Prediction/forecasting seizures algorithms will be evaluated in an epilepsy model established at Clinatec, using non-human primates, and the algorithms will be refined over time. Cooling the epileptic foci is an effective way to stop de seizure before generalization. This model allows us to test the efficacy of the algorithms in treating focal seizures. An assessment of hardware embedding design constraints would be conducted to facilitate next steps for the clinical device development. The project will benefit from a collaboration between Clinatec and DSYS/SSCE; and will be in line with upcoming activities of LETI’s artificial intelligence platform.
Real time low cost algorithms for brain computer interface with multiple degrees of freedom
The topic of the postdoctoral project is the optimization of BCI methods and algorithms for medical application in humans (quadriplegic subjects).
Namely the particular goal of the postdoctoral fellow will be optimization and the acceleration of calculation to allow multiple degrees of freedom (up to 26) in real time. Selecting the appropriate features subset will improve the computational efficiency and the quality of control. To this purpose the algorithms of sparse modeling will be applied.
To map ECoG recordings to the spatial-temporal-frequency space, continuous wavelet transform (CWT) is applied. Optimization will include the implementation of low cost CWT and C++ coding.
The project will include the test and the adaptation of BCI algorithms to wireless signal transmission with the implant WIMAGINE.
Finally the adaptation of algorithms to medical environment of quadriplegic subjects (the use of imaginary tasks, presence of stimuli in the signal, the restricted duration of experiments) will be under responsibility of postdoctoral scientist.