A Minimally Invasive Low-Power Platform for Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis

A growing trend in human-computer interaction is to integrate computational capabilities into wearable devices, to enable sophisticated and natural interaction modalities. Acting directly by decoding neural activity is a very natural way of interaction and one of the fundamental paradigms of brain computer interfaces (BCIs) as well. In this paper, we present a wearable Internet of Things node designed for BCI spelling. The system is based on visual evoked potentials detection and runs the canonical correlation analysis on a low power microcontroller. Neural data is acquired by an array of electroencephalography active dry electrodes, suitable for a minimally intrusive interface. To evaluate our solution, we optimized the system on eight subjects and tested it on five different subjects for four and eight stimuli, reaching a peak transfer rate of 1.57 b/s, comparable with those achieved by state-of-the-art nonembedded systems. The power consumption of the device is less than 30 mW, resulting in 122 h of operation with a standard 1000-mAh battery.