Restoration of Finger and Arm Movements Using Hybrid Brain/Neural Assistive Technology in Everyday Life Environments

Soekadar, Surjo R.; Nann, Marius; Crea, Simona; Trigili, Emilio; Gómez, Cristina; Opisso, Eloy; Cohen, Leonardo G.; Birbaumer, Niels; Vitiello, Nicola

doi:10.1007/978-3-030-05668-1_5

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…Moreover, there is increasing evidence that repeated brain/neural control of exoskeletons can trigger neural recovery ( Donati et al, 2016 ; Wagner et al, 2018 ). Therefore, a combination of both operational purposes, i.e., assistive and restorative use, was suggested ( Soekadar et al, 2019 ; Soekadar and Nann, 2020 ). Here, the assistive neural exoskeleton is used as a technical aid for the physiotherapist to train the patient in performing ADLs.…”

Section: Discussionmentioning

confidence: 99%

Feasibility and Safety of Bilateral Hybrid EEG/EOG Brain/Neural–Machine Interaction

Nann

Peekhaus

Angerhöfer

et al. 2020

Front. Hum. Neurosci.

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Cervical spinal cord injuries (SCIs) often lead to loss of motor function in both hands and legs, limiting autonomy and quality of life. While it was shown that unilateral hand function can be restored after SCI using a hybrid electroencephalography/electrooculography (EEG/EOG) brain/neural hand exoskeleton (B/NHE), it remained unclear whether such hybrid paradigm also could be used for operating two hand exoskeletons, e.g., in the context of bimanual tasks such as eating with fork and knife. To test whether EEG/EOG signals allow for fluent and reliable as well as safe and user-friendly bilateral B/NHE control, eight healthy participants (six females, mean age 24.1 ± 3.2 years) as well as four chronic tetraplegics (four males, mean age 51.8 ± 15.2 years) performed a complex sequence of EEG-controlled bilateral grasping and EOG-controlled releasing motions of two exoskeletons visually presented on a screen. A novel EOG command performed by prolonged horizontal eye movements (>1 s) to the left or right was introduced as a reliable switch to activate either the left or right exoskeleton. Fluent EEG control was defined as average “time to initialize” (TTI) grasping motions below 3 s. Reliable EEG control was assumed when classification accuracy exceeded 80%. Safety was defined as “time to stop” (TTS) all unintended grasping motions within 2 s. After the experiment, tetraplegics were asked to rate the user-friendliness of bilateral B/NHE control using Likert scales. Average TTI and accuracy of EEG-controlled operations ranged at 2.14 ± 0.66 s and 85.89 ± 15.81% across healthy participants and at 1.90 ± 0.97 s and 81.25 ± 16.99% across tetraplegics. Except for one tetraplegic, all participants met the safety requirements. With 88 ± 11% of the maximum achievable score, tetraplegics rated the control paradigm as user-friendly and reliable. These results suggest that hybrid EEG/EOG B/NHE control of two assistive devices is feasible and safe, paving the way to test this paradigm in larger clinical trials performing bimanual tasks in everyday life environments.

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Section: Discussionmentioning

confidence: 99%

Feasibility and Safety of Bilateral Hybrid EEG/EOG Brain/Neural–Machine Interaction

Nann

Peekhaus

Angerhöfer

et al. 2020

Front. Hum. Neurosci.

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“…BNCI systems are mainly applied to two scenarios: brain/neural control of assistive devices, such as wearable robots and prostheses, and use of BNCI systems for rehabilitation [24,25]. Lately, the combination of both scenarios, the assistive and rehabilitation ones, was suggested to improve the user acceptability and adoption of BNCI systems [27]. In this context, the optimization of BNCI systems in terms of mental work load and user engagement accessible through the subject’s physiological responses is a subject under investigation in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…In the context of the second scenario, in contrast, brain signals are used to induce neuroplasticity triggering neural recovery [25]. Recently, it was suggested to combine both approaches, the assistive and restorative approach, to increase user acceptability and broad adoption of BNCI-based rehabilitation [27]. For this purpose, EEG/EOG brain/neural control has to be optimized in terms of mental work load and user engagement accessible through the subject’s physiological responses.…”

Section: Introductionmentioning

confidence: 99%

Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton

Badesa

Díez

Catalán

et al. 2019

Sensors

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When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user’s physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject’s workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM.

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“…However, optimal frequency, dose and intensity of BCI training to maximize the rehabilitation effect remains unclear (Young et al, 2015, Lang et al, 2016. Recently, it was proposed that merging both strategies, i.e., introducing assistance to perform bimanual activities of daily living (ADL) in the context of stroke rehabilitation protocols, may substantially increase the impact of BCI technology and broaden its use in the medical field (Soekadar, 2020, Soekadar, 2019 However, BCI control tasks, e.g., performing repetitive MI of grasping movements, are cognitively demanding. As a consequence, decline in concentration and attention due to mental fatigue negatively affects BCI control performance (Myrden andChau, 2015, Curran andStokes, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…However, optimal frequency, dose and intensity of BCI training to maximize the rehabilitation effect remains unclear (Young et al, 2015, Soekadar et al, 2015, Lang et al, 2016). Recently, it was proposed that merging both strategies, i.e., introducing assistance to perform bimanual activities of daily living (ADL) in the context of stroke rehabilitation protocols, may substantially increase the impact of BCI technology and broaden its use in the medical field (Soekadar, 2020, Soekadar and Nann, 2020, Soekadar, 2019)…”

Section: Introductionmentioning

confidence: 99%

Heart rate variability predicts decline in sensorimotor rhythm control

Nann

Haslacher

Colucci

et al. 2021

Preprint

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Voluntary control of sensorimotor rhythms (SMR, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g., in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. It would thus be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme. Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline during voluntary control of SMR-ERD across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 minutes each. During the second run, task difficulty was gradually increased. While control performance (p = .18) and HRV (p = .16) remained unchanged across participants during the first run, during the second run, both measures declined over time at high correlation (performance: -0.61%/10s, p = 0; HRV: -0.007ms/10s, p < .001). We found that HRV Granger-caused BCI control performance (p < .001) exhibited predictive characteristics of HRV on an individual participant level. These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g., to individualize and optimize assistive BCI systems in stroke.

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Restoration of Finger and Arm Movements Using Hybrid Brain/Neural Assistive Technology in Everyday Life Environments

Cited by 14 publications

References 23 publications

Feasibility and Safety of Bilateral Hybrid EEG/EOG Brain/Neural–Machine Interaction

Feasibility and Safety of Bilateral Hybrid EEG/EOG Brain/Neural–Machine Interaction

Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton

Heart rate variability predicts decline in sensorimotor rhythm control

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