Immediate brain plasticity after one hour of brain–computer interface (BCI)

Nierhaus, Till; Vidaurre, Carmen; Sannelli, Claudia; Mueller, K.; Villringer, Arno

doi:10.1113/jp278118

Cited by 62 publications

(45 citation statements)

References 84 publications

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“…These data indicate that the rest of the variance is imputable to other reasons such as the algorithms or the paradigm [19, 32]. Moreover, clear evidences of neural plasticity in humans induced by BCI feedback have been recently demonstrated [93] also in clinical applications [94], so that it is reasonable to assume that a better BCI feedback would lead also users with small SMR amplitudes to enlarge the motor areas involved in the motor task, resulting in a higher SMR peak at rest and an easier SMR modulation. Here we want to remark, that we studied the particularities of EEG signals from many BCI users in order to understand how to improve the efficiency of BCI systems.…”

Section: Resultsmentioning

confidence: 99%

A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

et al. 2019

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Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.

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Section: Resultsmentioning

confidence: 99%

A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

et al. 2019

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“…Typically, BCI systems based of the modulation of SMR using MI tasks have lower rates of efficiency than other BCI paradigms based on evoked potentials such as event-related potentials (ERP) or steady-state visual potentials (SSVEP) (Nierhaus et al, 2019;Chen et al, 2015;Min et al, 2016). This is because MI-based BCI users normally need to acquire the skill to efficiently perform the MI tasks.…”

Section: Discussionmentioning

confidence: 99%

“…A modulation of brain activity in µ and β bands has been observed in relation to motor execution (Salmelin and Hari, 1994;Klopp et al, 2001), motor preparation (Pfurtscheller and Neuper, 1997;Pineda, 2005), somatosensory processing (Nikulin et al, 2007), and motor imagery (Neuper et al, 2005;Pfurtscheller et al, 2006;Bauer et al, 2015). And because of its malleability by diverse aspects of sensorimotor processing, µ rhythm serves as the main neuronal signal for sensorimotor BCI based on MI (Sannelli et al, 2019;Nierhaus et al, 2019;Buch et al, 2008;Waldert et al, 2008;Leuthardt et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance

Vidaurre

Haufe

Jorajuría

et al. 2020

Preprint

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Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. For BCIs based on the modulation of sensorimotor rhythms as measured by means of electroen-cephalography (EEG), about 20% of potential users do not obtain enough accuracy to gain reliable control of the system. This lack of efficiency of BCI systems to decode user’s intentions requires the identification of neuro-physiological factors determining ‘good’ and ‘poor’ BCI performers. Given that the neuronal oscillations, used in BCI, demonstrate rich a repertoire of spatial interactions, we hypothesized that neuronal activity in sensorimotor areas would define some aspects of BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in calibration and an online feedback session in the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post-as well as pre-stimulus connectivity in the calibration recordings is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study shows that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sen-sorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.

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“…Some BMI devices already “write” signals back to the brain, assisting a user, for example, with responsive movement of a robotic prosthesis or stimulating reminders to take medications when an epileptic is in danger of a seizure. Researchers have demonstrated that just one hour of training with a BMI causes significant alterations in their brain’s structure and function, thereby creating opportunities for therapeutic treatment for conditions like strokes but also the eventual capacity—as neurologists expand their understanding of the human brain—for targeted stimulation of movements, images, moods, memories, or even simple thoughts (Nierhaus et al 2020). One company has created a device that enables remote control of neural circuits in the brain through light signals and drug deliveries, a small brain implant, and a smartphone interface (University of Washington Health Sciences 2019).…”

Section: Commodification Of Bmismentioning

confidence: 99%

Brain–Machine Interfaces as Commodities: Exchanging Mind for Matter

Reilly

2020

Linacre Q

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Brain–machine interfaces (BMIs), which enable a two-way flow of signals, information, and directions between human neurons and computerized machines, offer spectacular opportunities for therapeutic and consumer applications, but they also present unique dangers to the safety, privacy, psychological health, and spiritual well-being of their users. The sale of these devices as commodities for profit exacerbates such issues and may subject the user to an unequal exchange with corporations. Catholic healthcare professionals and bioethicists should be especially concerned about the implications for the essential dignity of the persons using the new BMIs. Summary: The commercial sale of brain-machine interfaces (BMIs) generates and exacerbates problems for end-users' safety, psychological health, and spiritual well-being.

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Immediate brain plasticity after one hour of brain–computer interface (BCI)

Cited by 62 publications

References 84 publications

A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance

Brain–Machine Interfaces as Commodities: Exchanging Mind for Matter

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