Large-field study of ultra low-cost, non-invasive task level BMI

Current BMI technology requires significant development to enable patients with severe motor disabilities to obtain vital degrees of freedom in everyday life. State-of-the-art systems are expensive, require long training times and suffer from low patient uptake. We propose a non-invasive and ultra-low cost alternative - action intention decoding from 3D gaze signals. Building on our previous work, we present here a large field study (N=176 subjects) to understand how efficient our approach is at allowing subjects, from first use, to operate our BMI on the Pong BMI benchmark task. Within the first 30 seconds of first time use, the majority of subjects were able to play the arcade game pong against a computer. Subjects made on average 8.5±7.2 ball returns compared to the chance level of 2.6±2.5 obtained without input (mean±SD) and almost 5% even managed to beat the computer, despite having never used their eye-movements as a control input. This performance was achieved with members of the public at a scientific engagement event, not in stringent lab conditions and with minimal system calibration (30 s) and negligible user control learning (5 s countdown before ball released). This demonstrates the intuitive nature of gaze control and thus the clinical applicability of our approach.