Automatic Recognition and Feature Extraction of Motor-Evoked Potentials Elicited by Transcranial Magnetic Stimulation

Tecuapetla-Trejo, Jose E.; Cantillo-Negrete, Jessica; Valdés-Cristerna, Raquel; Carrillo‐Mora, Paul; Arias-Carrión, Óscar; Ortega-Robles, Emmanuel; Carino-Escobar, Ruben I.

doi:10.1007/978-3-030-30648-9_134

Cited by 2 publications

(1 citation statement)

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“…It has been reported that at least 20 trials of MEP recordings should be made for reliable assessment of single-pulse MEPs ( Biabani et al, 2018 ). Therefore, each session’s MEP peak to peak amplitude was computed using an automatic recognition software that averaged maximum and minimum values from the 20 trials with the fewest artifacts ( Tecuapetla-Trejo et al, 2020 ). MEP amplitude was used to assess the corticospinal tract integrity of patients before and after the interventions.…”

Section: Methodsmentioning

confidence: 99%

Brain-Computer Interface Coupled to a Robotic Hand Orthosis for Stroke Patients’ Neurorehabilitation: A Crossover Feasibility Study

Cantillo-Negrete

Carino-Escobar

Carrillo‐Mora

et al. 2021

Front. Hum. Neurosci.

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Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy. Seven subacute and three chronic stroke patients (M = 59.9 ± 12.8) with severe upper limb impairment were recruited in a crossover feasibility study to receive 1 month of BCI therapy and 1 month of conventional therapy in random order. The outcome measures were comprised of: Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), motor evoked potentials elicited by transcranial magnetic stimulation (TMS), hand dynamometry, and EEG. Additionally, BCI performance and user experience were measured. All measurements were acquired before and after each intervention. FMA-UE and ARAT after BCI (23.1 ± 16; 8.4 ± 10) and after conventional therapy (21.9 ± 15; 8.7 ± 11) were significantly higher (p < 0.017) compared to baseline (17.5 ± 15; 4.3 ± 6) but were similar between therapies (p > 0.017). Via TMS, corticospinal tract integrity could be assessed in the affected hemisphere of three patients at baseline, in five after BCI, and four after conventional therapy. While no significant difference (p > 0.05) was found in patients’ affected hand strength, it was higher after the BCI therapy. EEG cortical activations were significantly higher over motor and non-motor regions after both therapies (p < 0.017). System performance increased across BCI sessions, from 54 (50, 70%) to 72% (56, 83%). Patients reported moderate mental workloads and excellent usability with the BCI. Outcome measurements implied that a BCI intervention using a robotic hand orthosis as feedback has the potential to elicit neuroplasticity-related mechanisms, similar to those observed during conventional therapy, even in a group of severely impaired stroke patients. Therefore, the proposed BCI system could be a suitable therapy option and will be further assessed in clinical trials.

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Section: Methodsmentioning

confidence: 99%