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Sadras N, Pesaran B, Shanechi MM. Event detection and classification from multimodal time series with application to neural data. J Neural Eng 2024; 21:026049. [PMID: 38513289 DOI: 10.1088/1741-2552/ad3678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 03/21/2024] [Indexed: 03/23/2024]
Abstract
The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.
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Affiliation(s)
- Nitin Sadras
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Bijan Pesaran
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Thomas Lord Department of Computer Science, Alfred E. Mann Department of Biomedical Engineering, and the Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
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Sadras N, Sani OG, Ahmadipour P, Shanechi MM. Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making. J Neural Eng 2023; 20:056012. [PMID: 37524073 DOI: 10.1088/1741-2552/acec14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
Objective.When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.Approach.We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.Main results.We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.Significance.Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.
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Affiliation(s)
- Nitin Sadras
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Omid G Sani
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Parima Ahmadipour
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Neuroscience Graduate Program University of Southern California, Los Angeles, CA, United States of America
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Cinel C, Fernandez-Vargas J, Tremmel C, Citi L, Poli R. Enhancing performance with multisensory cues in a realistic target discrimination task. PLoS One 2022; 17:e0272320. [PMID: 35930533 PMCID: PMC9355224 DOI: 10.1371/journal.pone.0272320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 07/17/2022] [Indexed: 11/19/2022] Open
Abstract
Making decisions is an important aspect of people’s lives. Decisions can be highly critical in nature, with mistakes possibly resulting in extremely adverse consequences. Yet, such decisions have often to be made within a very short period of time and with limited information. This can result in decreased accuracy and efficiency. In this paper, we explore the possibility of increasing speed and accuracy of users engaged in the discrimination of realistic targets presented for a very short time, in the presence of unimodal or bimodal cues. More specifically, we present results from an experiment where users were asked to discriminate between targets rapidly appearing in an indoor environment. Unimodal (auditory) or bimodal (audio-visual) cues could shortly precede the target stimulus, warning the users about its location. Our findings show that, when used to facilitate perceptual decision under time pressure, and in condition of limited information in real-world scenarios, spoken cues can be effective in boosting performance (accuracy, reaction times or both), and even more so when presented in bimodal form. However, we also found that cue timing plays a critical role and, if the cue-stimulus interval is too short, cues may offer no advantage. In a post-hoc analysis of our data, we also show that congruency between the response location and both the target location and the cues, can interfere with the speed and accuracy in the task. These effects should be taken in consideration, particularly when investigating performance in realistic tasks.
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Affiliation(s)
- Caterina Cinel
- Brain Computer Interface and Neural Engineering Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
- * E-mail:
| | - Jacobo Fernandez-Vargas
- Brain Computer Interface and Neural Engineering Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Christoph Tremmel
- Brain Computer Interface and Neural Engineering Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
- WellthLab, Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
| | - Luca Citi
- Brain Computer Interface and Neural Engineering Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Riccardo Poli
- Brain Computer Interface and Neural Engineering Lab, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
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Tremmel C, Fernandez-Vargas J, Stamos D, Cinel C, Pontil M, Citi L, Poli R. A meta-learning BCI for estimating decision confidence. J Neural Eng 2022; 19. [PMID: 35738232 DOI: 10.1088/1741-2552/ac7ba8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/23/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of Brain-Computer Interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods. APPROACH We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from EEG and EOG data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants' data to produce a prediction algorithm that is then quickly tuned to new participants. We compared it with with the traditional single-subject training almost universally adopted in BCIs, a state-of-the-art transfer learning technique called Domain Adversarial Neural Networks (DANN), a transfer-learning adaptation of a zero-training method we used recently for a similar task, and with a simple baseline algorithm. MAIN RESULTS The meta-learning approach was significantly better than other approaches in most conditions, and much better in situations where limited data from a new participant are available for training/tuning. Meta-learning by biased regularisation allowed our BCI to seamlessly integrate information from past participants with data from a specific user to produce high-performance predictors. Its robustness in the presence of small training sets is a real-plus in BCI applications, as new users need to train the BCI for a much shorter period. SIGNIFICANCE Due to the variability and noise of EEG/EOG data, BCIs need to be normally trained with data from a specific participant. This work shows that even better performance can be obtained using our version of meta-learning by biased regularisation.
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Affiliation(s)
- Christoph Tremmel
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Jacobo Fernandez-Vargas
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Dimitrios Stamos
- Department of Computer Science, University College London, Malet Place, London, London, WC1E 6BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Caterina Cinel
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Massimiliano Pontil
- University College London, Malet Place, London, London, WC1E 6BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Luca Citi
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Riccardo Poli
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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