Toward passive BCI: asynchronous decoding of neural responses to direction- and angle-specific perturbations during a simulated cockpit scenario

Jalilpour, Shayan; Müller-Putz, Gernot

doi:10.1038/s41598-022-10906-5

Cited by 7 publications

(3 citation statements)

References 58 publications

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“…Extr. 2024, 6 200 have voluntary control over it, also called passive brain-computer interface (BCI), using electroencephalography (EEG) [5].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Light Conditions on Neural Affect Classification: A Deep Learning Approach

Zentner,

Barradas Chacon,

Wriessnegger

2024

MAKE

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Understanding and detecting human emotions is crucial for enhancing mental health, cognitive performance and human–computer interactions. This field in affective computing is relatively unexplored, and gaining knowledge about which external factors impact emotions could enhance communication between users and machines. Furthermore, it could also help us to manage affective disorders or understand affective physiological responses to human spatial and digital environments. The main objective of the current study was to investigate the influence of external stimulation, specifically the influence of different light conditions, on brain activity while observing affect-eliciting pictures and their classification. In this context, a multichannel electroencephalography (EEG) was recorded in 30 participants as they observed images from the Nencki Affective Picture System (NAPS) database in an art-gallery-style Virtual Reality (VR) environment. The elicited affect states were classified into three affect classes within the two-dimensional valence–arousal plane. Valence (positive/negative) and arousal (high/low) values were reported by participants on continuous scales. The experiment was conducted in two experimental conditions: a warm light condition and a cold light condition. Thus, three classification tasks arose with regard to the recorded brain data: classification of an affect state within a warm-light condition, classification of an affect state within a cold light condition, and warm light vs. cold light classification during observation of affect-eliciting images. For all classification tasks, Linear Discriminant Analysis, a Spatial Filter Model, a Convolutional Neural Network, the EEGNet, and the SincNet were compared. The EEGNet architecture performed best in all tasks. It could significantly classify three affect states with 43.12% accuracy under the influence of warm light. Under the influence of cold light, no model could achieve significant results. The classification between visual stimulus with warm light vs. cold light could be classified significantly with 76.65% accuracy from the EEGNet, well above any other machine learning or deep learning model. No significant differences could be detected between affect recognition in different light conditions, but the results point towards the advantage of gradient-based learning methods for data-driven experimental designs for the problem of affect decoding from EEG, providing modern tools for affective computing in digital spaces. Moreover, the ability to discern externally driven affective states through deep learning not only advances our understanding of the human mind but also opens avenues for developing innovative therapeutic interventions and improving human–computer interaction.

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“…Extr. 2024, 6 200 have voluntary control over it, also called passive brain-computer interface (BCI), using electroencephalography (EEG) [5].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Light Conditions on Neural Affect Classification: A Deep Learning Approach

Zentner,

Barradas Chacon,

Wriessnegger

2024

MAKE

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“…Additionally, no differences were found in the N1 peak amplitude and latency as a function of the perturbation direction. In our previous study [24], it was shown that the modulation of cortical responses elicited by balance perturbation carries directionand angle-specific information which can be classified with reliable accuracy. In another study by Adkin and colleagues [22], they applied displacement in predictable and unpredictable manners.…”

Section: Introductionmentioning

confidence: 99%

Balance perturbation and error processing elicit distinct brain dynamics

Jalilpour

Müller-Putz

2023

J. Neural Eng.

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Objective. The maintenance of balance is a complicated process in the human brain, which involves multisensory processing such as somatosensory and visual processing, motor planning and execution. It was shown that a specific cortical activity called perturbation-evoked potential (PEP) appears in the electroencephalogram (EEG) during balance perturbation. PEPs are primarily recognized by the N1 component with a negative peak localized in frontal and central regions. There has been a doubt in balance perturbation studies whether the N1 potential of perturbation is elicited due to error processing in the brain. The objective of this study is to test whether the brain perceives postural instability as a cognitive error by imposing two types of perturbations consisting of erroneous and correct perturbations. Approach. We conducted novel research to incorporate the experiment designs of both error and balance studies. To this end, participants encountered errors during balance perturbations at rare moments in the experiment. We induced errors by imposing perturbations to participants in the wrong directions and an erroneous perturbation was considered as a situation when the participant was exposed to an opposite direction of the expected/informed one. In correct perturbations, participants were tilted to the same direction, as they were informed. We analyzed the two conditions in time, time-frequency, and source domains. Main results. We showed that two error-related neural markers were derived from the EEG responses, including error positivity (Pe), and error-related alpha suppression (ERAS) during erroneous perturbations. Consequently, early neural correlates of perturbation cannot be interpreted as error-related responses. We discovered distinct patterns of conscious error processing; both Pe and ERAS are associated with conscious sensations of error. Significance. Our findings indicated that early cortical responses of balance perturbation are not associated with neural error processing of the brain, and errors induce distinct cortical responses that are distinguishable from brain dynamics of N1 potential.

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“…In many modern applications, such as manufacturing robots 1 , 2 , bionic robots 3 , 4 , medical robots 5 , 6 and aeronautical robots 7 , 8 , the mechanism is subject to external payloads and gravity of its own components which in turn induce deformations causing positioning errors. In order to improve motion accuracy and machining accuracy, stiffness analysis becomes an important solution.…”

Section: Introductionmentioning

confidence: 99%

Stiffness characteristics analysis of a Biglide industrial parallel robot considering the gravity of mobile platform and links

Guan

Zou

et al. 2023

Sci Rep

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For the machining process of industrial parallel robots, the gravity generated by the weight of mobile platform and links will lead to the deviation of the expected machining trajectory of the tool head. In order to evaluate this deviation and then circumvent it, it is necessary to perform the robotic stiffness model. However, the influence of gravity is seldom considered in the previous stiffness analysis. This paper presents an effective stiffness modeling method for industrial parallel robots considering the link/joint compliance, the mobile platform/link gravity, and the mass center position of each link. First, the external gravity corresponding to each component is determined by the static model under the influence of gravity and mass center position. Then, the corresponding Jacobian matrix of each component is obtained by the kinematic model. Subsequently, the compliance of each component is obtained by cantilever beam theory and FEA-based virtual experiments. In turn, the stiffness model of the whole parallel robot is determined and the Cartesian stiffness matrix of the parallel robot is calculated at several positions. Moreover, the principal stiffness distribution of the tool head in each direction over the main workspace is predicted. Finally, the validity of the stiffness model with gravity is experimentally proved by the comparison of the calculated stiffness and measured stiffness in identical conditions.

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Toward passive BCI: asynchronous decoding of neural responses to direction- and angle-specific perturbations during a simulated cockpit scenario

Cited by 7 publications

References 58 publications

The Impact of Light Conditions on Neural Affect Classification: A Deep Learning Approach

The Impact of Light Conditions on Neural Affect Classification: A Deep Learning Approach

Balance perturbation and error processing elicit distinct brain dynamics

Stiffness characteristics analysis of a Biglide industrial parallel robot considering the gravity of mobile platform and links

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