Face stimuli effectively prevent brain–computer interface inefficiency in patients with neurodegenerative disease

Kaufmann, Tobias; Schulz, Stefan M.; Köblitz, Anja; Renner, Gregor; Wessig, Carsten; Kübler, Andrea

doi:10.1016/j.clinph.2012.11.006

Cited by 142 publications

(123 citation statements)

References 21 publications

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“…All the services rely on a P300 spelling and control system. In the adopted speller, the highlighting happens with freely selectable images (famous faces) instead of just changing the color of the background (Kaufmann et al, 2013). Users interact with the system through two separated screens: one for the BCI matrix and one for the selected service.…”

Section: Methodsmentioning

confidence: 99%

“…An important number of research projects in the last number of years have contributed to improve brain-computer interface (BCI) technologies and a number of different applications for this alternative means of human-computer interaction have been produced (Lynch, 2002;Kaufmann et al, 2013). In the roadmap toward 2020 for BCI research proposed by Brunner et al (2015), applications are targeted at replacing, restoring, or improving the functions of people with some degree of disability as a key objective of future BCI research and innovation.…”

Section: Introductionmentioning

confidence: 99%

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Brain–Computer Interfaces on Track to Home: Results of the Evaluation at Disabled End-Users’ Homes and Lessons Learnt

et al. 2015

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The BackHome system is a multifunctional BCI system, the final outcome of a UserCentered Design approach, whose ambition is to move BCI systems from laboratories into the home of people in need for their independent home use. The paper presents the results of testing and evaluation of the BackHome system with end-users at their own homes. Results show moderate to good acceptance from end-users, caregivers, and therapists, which reported promising usability levels, good user satisfaction, and levels of control in the use of services and home support based on remote monitoring tools.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain–Computer Interfaces on Track to Home: Results of the Evaluation at Disabled End-Users’ Homes and Lessons Learnt

et al. 2015

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“…The concept of reported semi-deterministic synchrostates in a stochastic multivariate time-series data (in multi-channel EEG) and translating these states to complex networks to characterize the stimulus would attract the attention of other sub-branches of statistical physics. The proposed methodology of finding EEG synchrostates and the associated connectivity may be utilized in various future applications especially in the domain of BCI [82] and diagnosis of neurodegenerative diseases [66], [83].…”

Section: Synchrostate As a New Eeg Phase Synchronization Analysis Andmentioning

confidence: 99%

Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

Jamal

Das

Maharatna

et al. 2015

Physica A: Statistical Mechanics and its Applications

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Please cite this article as: W. Jamal, S. Das, K. Maharatna, I. Pan, D. Kuyucu, Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks, Physica A (2015), http://dx.doi.org/10.1016/j.physa. 2015.03.087 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract:Degree of phase synchronization between different Electroencephalogram (EEG) channels is known to be the manifestation of the underlying mechanism of information coupling between different brain regions. In this paper, we apply a continuous wavelet transform (CWT) based analysis technique on EEG data, captured during face perception tasks, to explore the temporal evolution of phase synchronization, from the onset of a stimulus. Our explorations show that there exists a small set (typically 3 -5) of unique synchronized patterns or synchrostates, each of which are stable of the order of milliseconds. Particularly, in the beta (β) band, which has been reported to be associated with visual processing task, the number of such stable states has been found to be three consistently. During processing of the stimulus, the switching between these states occurs abruptly but the switching characteristic follows a well-behaved and repeatable sequence. This is observed in a single subject analysis as well as a multiple-subject group-analysis in adults during face perception. We also show that although these patterns remain topographically similar for the general category of face perception task, the sequence of their occurrence and their temporal stability varies markedly between different face perception scenarios (stimuli) indicating towards different dynamical characteristics for information processing, which is stimulus-specific in nature. Subsequently, we translated these stable states into brain complex networks and derived some informative network measures for characterizing the degree of segregated processing and information integration in those synchrostates, leading to a new methodology for characterizing information processing in human brain. The proposed methodology of modelling the functional brain connectivity through these synchrostates may be viewed as a new way of quantitative characterization of the cognitive ability of the subject, stimuli and information integration/segregation capability.

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“…The robot's hands on the screen correspond to the give and grasp actions, while the six arrows correspond to different movement commands (Figure 1). Each stimulus is represented by a flashing image of a famous face, Albert Einstein, which replace one of the symbol of the interface accordingly to the oddball stimulus, to help engage the user and elicit more robust ERPs (Kaufmann et al, 2012).…”

Section: The Bci-robot Systemmentioning

confidence: 99%

“…The real-time EEG was amplified, filtered and analyzed to extract the P300 and other ERPs such as the N170 and N400f, which can improve classification accuracy with presentation of famous faces (Kaufmann et al, 2012).…”

Section: The Bci-robot Systemmentioning

confidence: 99%

Reaching and grasping a glass of water by locked-in ALS patients through a BCI-controlled humanoid robot

Spataro¹,

Sorbello²,

Tramonte³

et al. 2015

Journal of the Neurological Sciences

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Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling. In the present study, locked-in ALS patients used a BCI system to directly control the humanoid robot NAO (Aldebaran Robotics, France) with the aim of reaching and grasping a glass of water. Four ALS patients and four healthy controls were recruited and trained to operate this humanoid robot through a P300-based BCI. A few minutes training was sufficient to efficiently operate the system in different environments. Three out of the four ALS patients and all controls successfully performed the task with a high level of accuracy. These results suggest that BCI-operated robots can be used by locked-in ALS patients as an artificial alter-ego, the machine being able to move, speak and act in his/her place.

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Face stimuli effectively prevent brain–computer interface inefficiency in patients with neurodegenerative disease

Cited by 142 publications

References 21 publications

Brain–Computer Interfaces on Track to Home: Results of the Evaluation at Disabled End-Users’ Homes and Lessons Learnt

Brain–Computer Interfaces on Track to Home: Results of the Evaluation at Disabled End-Users’ Homes and Lessons Learnt

Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

Reaching and grasping a glass of water by locked-in ALS patients through a BCI-controlled humanoid robot

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