Hybrid Dry EEG‑fNIRS Fusion for Low‑Latency Real‑Time BCI Control of Robotic Prosthetics

Abstract - We present, for the first time, a fully commercializable hybrid brain‑computer interface (BCI) architecture that fuses dry electroencephalography (EEG) with functional near‑infrared spectroscopy (fNIRS) to enable low‑latency, high‑accuracy control of powered prosthetic limbs. Leveraging proven signal‑processing pipelines - band‑pass filtering, common spatial pattern (CSP) extraction, and deep convolutional neural networks (CNNs) - the system achieves 92 % classification accuracy on a 3‑class motor‑imagery task with an overall response time of 210 ms.