Probabilistic simulation framework for EEG-based BCI design

Orhan, Umut; Nezamfar, Hooman; Akçakaya, Murat; Erdoğmuş, Deni̇z; Higger, Matt; Moghadamfalahi, Mohammad; Fowler, Andrew; Roark, Brian; Oken, Barry; Fried-Oken, Melanie

doi:10.1080/2326263x.2016.1252621

Cited by 12 publications

(10 citation statements)

References 32 publications

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“…To assess the performance of the proposed query selection method, an language-model-assisted EEG-based BCI typing system called RSVP Keyboard ™ [35] has been used as a test framework. Ten healthy participants (six females and four males), 20-35 years old were recruited under IRB-130107 protocol approved by Northeastern University.…”

Section: Resultsmentioning

confidence: 99%

“…For our simulations we used conditional evidence distributions which are learned in the calibration session. More details about the simulation framework can be found in [35] study. During simulations, through Monte Carlo simulations samples from these conditional distributions were drawn to type ‘ O ’ and ‘ C ’ for each simulation in the phrase ‘ IT_OCCURRED_RANDOMLY ’.…”

Section: Resultsmentioning

confidence: 99%

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Optimal Query Selection Using Multi-Armed Bandits

Kocanaogullari

Marghi

Akçakaya

et al. 2018

IEEE Signal Process. Lett.

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Query selection for latent variable estimation is conventionally performed by opting for observations with low noise or optimizing information theoretic objectives related to reducing the level of estimated uncertainty based on the current best estimate. In these approaches, typically the system makes a decision by leveraging the current available information about the state. However, trusting the current best estimate results in poor query selection when truth is far from the current estimate, and this negatively impacts the speed and accuracy of the latent variable estimation procedure. We introduce a novel sequential adaptive action value function for query selection using the multi-armed bandit (MAB) framework which allows us to find a tractable solution. For this adaptive-sequential query selection method, we analytically show: (i) performance improvement in the query selection for a dynamical system, (ii) the conditions where the model outperforms competitors. We also present favorable empirical assessments of the performance for this method, compared to alternative methods, both using Monte Carlo simulations and human-in-the-loop experiments with a brain computer interface (BCI) typing system where the language model provides the prior information.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Optimal Query Selection Using Multi-Armed Bandits

Kocanaogullari

Marghi

Akçakaya

et al. 2018

IEEE Signal Process. Lett.

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“…These samples are used to estimate the state. For the details of this simulation framework, the reader is referred to Orhan’s work [25].…”

Section: Methodsmentioning

confidence: 99%

On Analysis of Active Querying for Recursive State Estimation

Kocanaogullari

Erdoğmuş

Akçakaya

2018

IEEE Signal Process. Lett.

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In stochastic linear/non-linear active dynamic systems, states are estimated with the evidence through recursive measurements in response to queries of the system about the state to be estimated. Therefore, query selection is essential for such systems to improve state estimation accuracy and time. Query selection is conventionally achieved by minimization of the evidence variance or optimization of various information theoretic objectives. It was shown that optimization of mutual information-based objectives and variance-based objectives arrive at the same solution. However, existing approaches optimize approximations to the intended objectives rather than solving the exact optimization problems. To overcome these shortcomings, we propose an active querying procedure using mutual information maximization in recursive state estimation. First we show that mutual information generalizes variance based query selection methods and show the equivalence between objectives if the evidence likelihoods have unimodal distributions. We then solve the exact optimization problem for query selection and propose a query (measurement) selection algorithm. We specifically formulate the mutual information maximization for query selection as a combinatorial optimization problem and show that the objective is sub-modular, therefore can be solved efficiently with guaranteed convergence bounds through a greedy approach. Additionally, we analyze the performance of the query selection algorithm by testing it through a brain computer interface typing system.

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“…This mode is base on a proposed probabilistic simulation framework in [22]. A Copy Phrase task is simulated without user intervention.…”

Section: (3) Simulationmentioning

confidence: 99%

“…Calibration data consist of real ERP-FRP EEG data from a single-channel (Fz) and multiple channels for 10 healthy participants. Using the probability distributions learned for each model in study IV-B, the probabilistic simulation framework in [22] has been used by taking 100 Monte-Carlo simulations of a Copy Phrase task with 10 predefined sentences. The typing performances for all models are reported by ITR (bit/sequence).…”

Section: Simulationsmentioning

confidence: 99%

An Event-Driven AR-Process Model for EEG-Based BCIs With Rapid Trial Sequences

Gonzalez-Navarro

Marghi

Azari

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Electroencephalography (EEG) is an effective non-invasive measurement method to infer user intent in brain-computer interface (BCI) systems for control and communication, however these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many factors impact system performance, including inadequate training datasets and models' ignorance of the temporal dependency of brain responses to serial stimuli. Here, we propose a signal model for event-related responses in EEG evoked with a rapid sequence of stimuli in BCI applications. The model describes EEG as a superposition of impulse responses time-locked to stimuli corrupted with an autoregressive noise process. The performance of the signal model is assessed in the context of RSVP Keyboard, a language-model-assisted EEG-based BCI for typing. EEG data obtained for model calibration from 10 healthy participants is used to fit and compare two models: the proposed sequence-based EEG model and the trial-based featureclass-conditional distribution model that ignores temporal dependencies, which has been used in previous work. Simulation studies indicate that the earlier model that ignores temporal dependencies may be causing drastic reductions in achievable information transfer rate (ITR). Furthermore, the proposed model, with better regularization, may achieve improved accuracy with fewer calibration data samples, potentially helping to reduce calibration time. Specifically, results Personal use is permitted, but republication/redistribution requires IEEE permission.

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Probabilistic simulation framework for EEG-based BCI design

Cited by 12 publications

References 32 publications

Optimal Query Selection Using Multi-Armed Bandits

Optimal Query Selection Using Multi-Armed Bandits

On Analysis of Active Querying for Recursive State Estimation

An Event-Driven AR-Process Model for EEG-Based BCIs With Rapid Trial Sequences

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