Modeling the P300-based brain-computer interface as a channel with memory

Mayya, Vaishakhi; Mainsah, Boyla O.; Reeves, Galen

doi:10.1109/allerton.2016.7852206

Cited by 2 publications

(5 citation statements)

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“…The performance measures were estimated based on data from the test run and include: accuracy, the EST, the mean character selection time, bit rate [53] and detectability indices. The accuracy, A , is the percentage of characters spelled correctly during testing.…”

Section: Methodsmentioning

confidence: 99%

“…Within a noisy channel framework, other channel models that explicitly model refractory effects during the ERP elicitation process can be used to optim ize codebook design. A recent extension of this work proposes an alternative theoretical framework based on finite-state channels with memory to model and analyze the impact of refractory effects on BCI performance [54]. Preliminary results from simulations show the potential to use this framework to design codebooks that closely achieve channel capacity within the refractory effect limitations defined by a specific channel model.…”

Section: Analysis Of Codebook Performancementioning

confidence: 99%

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Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

et al. 2017

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Objective The role of a brain-computer interface (BCI) is to discern a user’s intended message by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. Approach We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. Main results With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm. Significance By accounting for refractory effects, an information-theoretic approach can be exploited to significantly improve BCI performance across a wide range of performance levels.

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Section: Methodsmentioning

confidence: 99%

Section: Analysis Of Codebook Performancementioning

confidence: 99%

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

et al. 2017

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“…The probabilistic mapping from Z n to Y n models the noise induced by the EEG measurement and classification process. The case L = 1 was studied in our previous work [16]. In this paper, we study the behavior of channels with general L.…”

Section: A P300 Speller Channel Modelmentioning

confidence: 99%

“…The case L = 1 was studied in our previous work [16]. In this paper, we study the behavior of channels with general L.…”

Section: A P300 Speller Channel Modelmentioning

confidence: 99%

“…We remark that the distribution described in Proposition 2 might not be the only distribution that achieves the maximum. In [16], we showed that in a channel with L = 1, when the input is a first order Markov source, there are at least two input transition matrices for which the maximum information rate is achieved.…”

Section: A Analysis Of the Optimal Input Distributionmentioning

confidence: 99%

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Information-theoretic analysis of refractory effects in the P300 speller

Mayya

Mainsah

Reeves

2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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The P300 speller is a brain-computer interface that enables people with neuromuscular disorders to communicate based on eliciting event-related potentials (ERP) in electroencephalography (EEG) measurements. One challenge to reliable communication is the presence of refractory effects in the P300 ERP that induces temporal dependence in the user's EEG responses. We propose a model for the P300 speller as a communication channel with memory. By studying the maximum information rate on this channel, we gain insight into the fundamental constraints imposed by refractory effects. We construct codebooks based on the optimal input distribution, and compare them to existing codebooks in literature.

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Modeling the P300-based brain-computer interface as a channel with memory

Cited by 2 publications

References 16 publications

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

Information-theoretic analysis of refractory effects in the P300 speller

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