Optogenetic Brain Interfaces using Neurostimulation Technology

Optogenetic brain interfaces represent a cuttingedge convergence of neuroscience and photonics, enabling precise modulation and observation of neural activity through light-sensitive proteins. However, the real-time imaging and signal acquisition in these systems are often compromised by blurring. To resolve this, Gated Recurrent Unit models are utilized to categorize neural responses, enabling the identification of functional brain regions. Furthermore, Wiener filtering is employed to reduce additive noise and correct blurring artifacts, high-quality data. Segmentation, Ada Boosting, and Region-Based Segmentation methods are then applied to isolate active neural regions, distinguishing meaningful optogenetic signals from background structures. Following this, feature Extraction DenseNet techniques are used to quantify spatial and temporal patterns of neural activation, capturing key metrics such as intensity variation, response latency, and morphological structure. Finally, the performance is evaluated using standard classification metrics, including accuracy (94.7%), precision (93.5%), recall (95.2%), F1-score (94.3%), and Specificity (96.1%).