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<p>Brain-computer interfaces (BCIs) have the potential to optimise robotic-assisted gait rehabilitation by closing the loop between user and device. Developing such systems requires reliable methods for classifying walking and its component phases to enable real-time feedback. However, research into electroencephalography (EEG)-based gait classification remains limited, with no consensus on optimal methodological or classification approaches. This scoping review aimed to identify (i) gait states and phases classified to date, (ii) methodological processes employed in gait and gait phase classification and (iii) classification systems used. A pre-registered scoping review was conducted. SCOPUS, EMBASE, PubMed and Web of Science were searched. Studies investigating EEG-based gait or gait phase classification, with or without robotic assistance were included. From 15,915 unique studies, 62 were included. Ten studies classified gait phases, with most limited to two phases (<i>n</i> = 5). The majority involved treadmill walking (<i>n</i> = 8), with only two investigating overground walking. Only one study addressed gait phase classification (swing versus stance) during robotic-assisted gait training (RAGT), achieving a maximum accuracy of 83.06%. No studies investigated gait phase classification in a neurological population. Almost all gait phase studies used offline analysis (<i>n</i> = 9). One study performed online analysis, achieving an accuracy of 82.3% for swing versus stance classification. The remaining studies classified gait against other forms of movement. Sensorimotor area electrodes were most frequently used for classification and sensorimotor regions consistently exhibited gait-related discriminative EEG features. Collation of methodological processes identified little consensus within the field. Furthermore, classification accuracy was variable. This scoping review summarises EEG-based gait classification systems, highlighting the need for methodological consensus. Knowledge gaps include the classification of more than two gait phases during free overground walking and limited RAGT-based studies. Notably, gait phase classification research in neurological populations is entirely lacking. Addressing these gaps is critical to advance BCIs for gait rehabilitation.</p>

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The classification of walking and phases of gait using EEG: a scoping review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Cormac Ryan,
  • Conor White,
  • Olive Lennon

摘要

Brain-computer interfaces (BCIs) have the potential to optimise robotic-assisted gait rehabilitation by closing the loop between user and device. Developing such systems requires reliable methods for classifying walking and its component phases to enable real-time feedback. However, research into electroencephalography (EEG)-based gait classification remains limited, with no consensus on optimal methodological or classification approaches. This scoping review aimed to identify (i) gait states and phases classified to date, (ii) methodological processes employed in gait and gait phase classification and (iii) classification systems used. A pre-registered scoping review was conducted. SCOPUS, EMBASE, PubMed and Web of Science were searched. Studies investigating EEG-based gait or gait phase classification, with or without robotic assistance were included. From 15,915 unique studies, 62 were included. Ten studies classified gait phases, with most limited to two phases (n = 5). The majority involved treadmill walking (n = 8), with only two investigating overground walking. Only one study addressed gait phase classification (swing versus stance) during robotic-assisted gait training (RAGT), achieving a maximum accuracy of 83.06%. No studies investigated gait phase classification in a neurological population. Almost all gait phase studies used offline analysis (n = 9). One study performed online analysis, achieving an accuracy of 82.3% for swing versus stance classification. The remaining studies classified gait against other forms of movement. Sensorimotor area electrodes were most frequently used for classification and sensorimotor regions consistently exhibited gait-related discriminative EEG features. Collation of methodological processes identified little consensus within the field. Furthermore, classification accuracy was variable. This scoping review summarises EEG-based gait classification systems, highlighting the need for methodological consensus. Knowledge gaps include the classification of more than two gait phases during free overground walking and limited RAGT-based studies. Notably, gait phase classification research in neurological populations is entirely lacking. Addressing these gaps is critical to advance BCIs for gait rehabilitation.