Brain-Computer Interfaces (BCI): Restoration of Movement and Thought from Neuroelectric and Metabolic Brain Activity

Soekadar, Surjo R.; Haagen, Klaus; Birbaumer, Niels

doi:10.1007/978-3-540-74479-5_11

Cited by 7 publications

(3 citation statements)

References 76 publications

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“…Les interfaces cerveau-machine peuvent aussi avoir une valeur en rééducation neuronale, pour les patients sévérement handicapés, en renforçant l'usage des chemins neuronaux endommagés en tirant parti de la plasticité du système nerveux (Dobkin, 2007). Enfin l'utilisation des interfaces cerveau-machine pour la rééducation motrice ne doit pas être négligée , (Soekadar et al, 2008).…”

Section: Interface Cerveau-ordinateur Et Approche Thérapeutiqueunclassified

Les interfaces Cerveau-Machine pour la palliation du handicap moteur sévère

Bekaert¹,

Botte-Lecocq²,

François³

et al. 2009

STH

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Section: Interface Cerveau-ordinateur Et Approche Thérapeutiqueunclassified

Les interfaces Cerveau-Machine pour la palliation du handicap moteur sévère

Bekaert¹,

Botte-Lecocq²,

François³

et al. 2009

STH

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“…BCI2000 thus represents a flexible framework for designing BCI experiments. This software has already been used in several published studies on BCI [40][41][42] and is currently been used by about 300 laboratories worldwide. BCI2000 has however been less used for neurofeedback applications although it does embed the main required features.…”

Section: Independent Component Analysismentioning

confidence: 99%

NeuroTRIP: A Framework for Bridging between Open Source Software. Application to Training a Brain Machine Interface

Grandchamp

Delorme

2009

2009 Fifth International Conference on Signal Image Technology and Internet Based Systems

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Within the field of Brain Computer Interface, the sub-field of Neurofeedback that consists in training subjects to control specific brain rhythms is gaining momentum. Spatial filtering of brain source activities using Independent Component Analysis (ICA) may help subjects to specifically train specific brain regions. Here we propose an integrated platform built on top of two Open Source software namely BCI2000 and EEGLAB, to design and run source-based ICA Neurofeedback experiments. Our public software NeuroTRIP (Neurofeedback TRaining Ica Program) automates the sequence of procedure necessary to train a computer program to recognize brain sources and apply this training to isolate these sources in real time and provide feedback to subjects. In addition to provide an innovative technique for EEG Brain Machine Interface protocols, it will offer a flexible open source tool to conduct controlled experiments on neurotherapy methodology and results.

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“…As an alternative to surgical interventions, recent advancements in neurotechnology and robotics opened up new possibilities to restore hand and arm function after cervical SCI ( Soekadar et al, 2016 ) or stroke ( Soekadar et al, 2008 , 2015a ; Nann et al, 2020 ). It was shown that exoskeletons or functional electrical stimulation (FES) of paralyzed muscles can enhance grasping force and improve hand function in tetraplegics ( Ragnarsson, 2008 ; Ho et al, 2014 ; Yun et al, 2017 ; Cappello et al, 2018 ).…”

Section: Introductionmentioning

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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Brain-Computer Interfaces (BCI): Restoration of Movement and Thought from Neuroelectric and Metabolic Brain Activity

Cited by 7 publications

References 76 publications

Les interfaces Cerveau-Machine pour la palliation du handicap moteur sévère

Les interfaces Cerveau-Machine pour la palliation du handicap moteur sévère

NeuroTRIP: A Framework for Bridging between Open Source Software. Application to Training a Brain Machine Interface

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

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