Figure - PMC

Skip to main content

View full-text article in PMC

. Author manuscript; available in PMC: 2022 Dec 9.

Published in final edited form as: IEEE Trans Biomed Circuits Syst. 2021 Dec 9;15(5):877–897. doi: 10.1109/TBCAS.2021.3112756

Fig. 4: — Hardware architectures and chip micrographs of ML-embedded neural prostheses for epilepsy: (a) Linear dual-detector SVM classifier and closed-loop transcranial neurostimulator [13], (b) non-linear SVM-based seizure detector [14], (c) linear least square (LLS) classifier and closed-loop stimulator [17], (d) ridge regression classifier (RRC) and closed-loop stimulator [88]. (e) Gradient-boosted tree ensemble for seizure detection [12], (f) exponentially decaying-memory SVM and closed-loop stimulator [16], (g) AdaBoost decision tree classifier and closed-loop stimulator [29], (h) two-level coarse/fine classifier and closed-loop stimulator [89].

Fig. 4: — Hardware architectures and chip micrographs of ML-embedded neural prostheses for epilepsy: (a) Linear dual-detector SVM classifier and closed-loop transcranial neurostimulator [13], (b) non-linear SVM-based seizure detector [14], (c) linear least square (LLS) classifier and closed-loop stimulator [17], (d) ridge regression classifier (RRC) and closed-loop stimulator [88]. (e) Gradient-boosted tree ensemble for seizure detection [12], (f) exponentially decaying-memory SVM and closed-loop stimulator [16], (g) AdaBoost decision tree classifier and closed-loop stimulator [29], (h) two-level coarse/fine classifier and closed-loop stimulator [89].