This thesis presents an efficient algorithm by combining symbolic dynamics and brain-inspired hyperdimensional (HD) computing for both seizure onset detection and identification of ictogenic (= seizure generating) brain regions from intracranial electroencephalography (iEEG). Moreover, the simplicity of the algorithm eases its implementation on an embedded AI computing device platform (e.g. the NVIDIA Jetson TX2 Module) for long term operation. The proposed algorithm provides: (1) a unified method for both learning and classification tasks with end-to-end binary operations; (2) one-shot learning from seizure examples; (3) linear computational scalability to any number of electrodes; (4) generation of transparent codes with interpretable features; (5) a simple embedded implementation which is fast and energy efficient.