Mental State Estimation for Brain--Computer Interfaces

Das, Kishore Kumar; Rizzuto, Daniel S.; Nenadic, Zoran

doi:10.1109/tbme.2009.2022948

Cited by 19 publications

(9 citation statements)

References 67 publications

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“…To further smooth the state transitions, the calibration procedure often required averaging the posterior probabilities P(W f ⋆ ) across two or three consecutive 750-ms data segments. Another option is to smooth the posterior probabilities through a recursive Bayesian update [32]: where f ⋆ k−1 and f ⋆ k are the observed EEG features across two consecutive data segments. The feature extraction transformations, the parameters of the likelihood function, and the thresholds T I and T W were then saved for each subject.…”

Section: Self-paced Bci Control Of Ambulation In a Virtual Reality Environmentmentioning

confidence: 99%

Brain–computer interfaces for human gait restoration

Nenadic

2021

Control Theory Technol.

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In this review article, we present more than a decade of our work on the development of brain–computer interface (BCI) systems for the restoration of walking following neurological injuries such as spinal cord injury (SCI) or stroke. Most of this work has been in the domain of non-invasive electroencephalogram-based BCIs, including interfacing our system with a virtual reality environment and physical prostheses. Real-time online tests are presented to demonstrate the ability of able-bodied subjects as well as those with SCI to purposefully operate our BCI system. Extensions of this work are also presented and include the development of a portable low-cost BCI suitable for at-home use, our ongoing efforts to develop a fully implantable BCI for the restoration of walking and leg sensation after SCI, and our novel BCI-based therapy for stroke rehabilitation.

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Section: Self-paced Bci Control Of Ambulation In a Virtual Reality Environmentmentioning

confidence: 99%

Brain–computer interfaces for human gait restoration

Nenadic

2021

Control Theory Technol.

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“…problem 27 , a principal component analysis (PCA) based non-linear feature extraction technique-'Classwise Principal Component Analysis' (CPCA) 27 has been used to reduce the dimensionality of the EEG signals and extract informative features. CPCA has been used successfully in previous studies to extract the multivariate pattern of neural signals [28][29][30][31][32][33] . The main goal of CPCA is to identify and discard non-informative subspace in data by applying principal component based analysis to each class.…”

Section: Methodsmentioning

confidence: 99%

Wisdom of crowds benefits perceptual decision making across difficulty levels

Roy

Mazumder

Das

2021

Sci Rep

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Decades of research on collective decision making has claimed that aggregated judgment of multiple individuals is more accurate than expert individual judgement. A longstanding problem in this regard has been to determine how decisions of individuals can be combined to form intelligent group decisions. Our study consisted of a random target detection task in natural scenes, where human subjects (18 subjects, 7 female) detected the presence or absence of a random target as indicated by the cue word displayed prior to stimulus display. Concurrently the neural activities (EEG signals) were recorded. A separate behavioural experiment was performed by different subjects (20 subjects, 11 female) on the same set of images to categorize the tasks according to their difficulty levels. We demonstrate that the weighted average of individual decision confidence/neural decision variables produces significantly better performance than the frequently used majority pooling algorithm. Further, the classification error rates from individual judgement were found to increase with increasing task difficulty. This error could be significantly reduced upon combining the individual decisions using group aggregation rules. Using statistical tests, we show that combining all available participants is unnecessary to achieve minimum classification error rate. We also try to explore if group aggregation benefits depend on the correlation between the individual judgements of the group and our results seem to suggest that reduced inter-subject correlation can improve collective decision making for a fixed difficulty level.

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“…Since the neural data is high dimensional and suffers from small sample size problem [49], a recently proposed principal component analysis (PCA) based non-linear feature extraction technique -’Classwise Principal Component Analysis –’ (CPCA) [49] is used. CPCA been used previously to efficiently reduce the dimensionality of the EEG signals and extract informative features [45, 50–54]. The main goal of CPCA is to identify and discard non-informative subspace in data by applying principal component based analysis to each class.…”

Section: Methodsmentioning

confidence: 99%

Neural Mediators of Altered Perceptual Choice and Confidence Using Social Information

Roy

Giri

Chowdhury

et al. 2019

Preprint

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Understanding how individuals utilize social information while making perceptual decisions and how it affects their decision confidence is crucial in a society. Till date, very little is known about perceptual decision making in humans under the influence of social cues and the associated neural mediators. The present study provides empirical evidence of how individuals get manipulated by social cues while performing a face/car identification task. Subjects were significantly influenced by what they perceived as decisions of other subjects while the cues in reality were manipulated independently from the stimulus. Subjects in general tend to increase their decision confidence when their individual decision and social cues coincide, while their confidence decreases when cues conflict with their individual judgments often leading to reversal of decision. Using a novel statistical model, it was possible to rank subjects based on their propensity to be influenced by social cues. This was subsequently corroborated by analysis of their neural data. Neural time series analysis revealed no significant difference in decision making using social cues in the early stages unlike neural expectation studies with predictive cues. Multivariate pattern analysis of neural data alludes to a potential role of frontal cortex in the later stages of visual processing which appeared to code the effect of social cues on perceptual decision making. Specifically medial frontal cortex seems to play a role in facilitating perceptual decision preceded by conflicting cues.

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Mental State Estimation for Brain--Computer Interfaces

Cited by 19 publications

References 67 publications

Brain–computer interfaces for human gait restoration

Brain–computer interfaces for human gait restoration

Wisdom of crowds benefits perceptual decision making across difficulty levels

Neural Mediators of Altered Perceptual Choice and Confidence Using Social Information

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