The classification of walking and phases of gait using EEG: a scoping review.
Cormac Ryan, Conor White, Olive Lennon
PMID 42380885WHAT IT FOUND
EEG-based studies could distinguish walking from rest in lab settings, but gait phase classification is sparse, mostly treadmill and healthy adults, and not ready for clinical robotic gait training.
Key findings
01This review included 62 studies; only ten classified gait phases, and no study classified gait phases in people with central neurological pathology.
02Walking versus static or resting states was the easiest task, with reported performance up to 100% offline and 85.6% online.
03The authors concluded current classification is not sufficiently robust or validated in real time for clinical robotic gait training.
STILL TO COME
How it was doneWhat they foundWhat it means for PTs
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What it does not show
The review only included English language studies, so relevant work in other languages may be missing. It excluded studies focused only on imagined movement or movement intention, so it does not cover that larger body of work. The included studies used very different methods, tasks, settings and metrics, so their results cannot be compared directly. Most participants were healthy adults, and no included study classified gait phases in people with central neurological pathology. Most studies were done in controlled indoor settings and analysed data offline, so they do not show how classification would work during real time gait training. The review did not appraise the quality of the included studies.
Declared interests
The paper states that the funder was Science Foundation Ireland.
The easy way to misread this
Do not read the reported performance figures as evidence that EEG-controlled robotic gait training is ready for clinical use. Most studies were offline, healthy, indoor, and no gait phase classification was done in neurological patients.