Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail?

Waldert, Stephan

doi:10.3389/fnins.2016.00295

Cited by 114 publications

(65 citation statements)

References 42 publications

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“…From the signal recording point of view, the ability to accurately decode all the degrees of freedom of the arm and leg to provide dexterous and natural control (e.g., grasping and manipulation of different objects) still remains a challenge. While implantable electrodes can provide a very accurate decoding of movement intentions, user acceptance of invasive BMIs is still low, mainly due to the risks related to the neurosurgery and postsurgical complications (Waldert, 2016). On the other hand, noninvasive approaches have generally provided low accuracies.…”

Section: Current Challengesmentioning

confidence: 99%

Brain-machine interfaces for rehabilitation in stroke: A review

López‐Larraz

Sarasola-Sanz

Irastorza-Landa

et al. 2018

NRE

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Section: Current Challengesmentioning

confidence: 99%

Brain-machine interfaces for rehabilitation in stroke: A review

López‐Larraz

Sarasola-Sanz

Irastorza-Landa

et al. 2018

NRE

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“…For people with chronic paralysis following a severe spinal cord injury, surgical implants that record activity directly from the brain can provide a means of interacting with the environment, such as controlling a prosthetic (Mak and Wolpaw, 2009;Shih et al, 2012). However, the need to implant electrodes limits the applications of this invasive approach (Waldert, 2016). The use of neuroimaging modalities as non-invasive BCI devices has garnered attention for applications such as assessing cognition in patients with disorders of consciousness (DOC), providing rudimentary communication for patients in a completely locked-in state, and as a feedback tool for stroke therapy (Naseer and Hong, 2015a;Kurz et al, 2018;Rupawala et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

et al. 2020

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Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional nearinfrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for "mental communication" on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for "yes" and to stay relaxed for "no." The change in the mean timeof-flight of photons was used to calculate the change in concentrations of oxy-and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as "yes" or "no" responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.

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“…Such tasks are typically unrelated to the current activity and their use imposes additional mental load and decreases the fluency of BCI use. Invasive BCIs are promising, but high risks and costs associated with brain surgery require further efforts to make this technology acceptable even for severely paralyzed patients (Lahr et al, 2015; Bowsher et al, 2016; Waldert, 2016). …”

Section: Introductionmentioning

confidence: 99%

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

et al. 2016

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We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200–500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for paralyzed and healthy users, the EBCI “Wish Mouse.”

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Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail?

Cited by 114 publications

References 42 publications

Brain-machine interfaces for rehabilitation in stroke: A review

Brain-machine interfaces for rehabilitation in stroke: A review

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

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