Boyla Octavie Mainsah

Conference Proceedings of the Aaai Conference on Artificial Intelligence · January 1, 2026 The current standard for training brain-computer interface (BCI) machine learning models is user-specific. There is a high interest in developing generic models that are trained on data from other users to minimize BCI calibration time; however, this is li ... Full text Cite

Assessing the Impact of Population Data Domain Differences on Transfer Learning in P300-based Brain-Computer Interfaces

Conference Proceedings of the Aaai Conference on Artificial Intelligence · April 11, 2025 Brain-computer interfaces (BCIs) can provide a means of communication for individuals with severe neuromuscular diseases, the target end-users. While personalized BCI machine learning models are the current standard, models trained on data from other users ... Full text Cite

Suppressing reverberation in cochlear implant stimulus patterns using time-frequency masks based on phoneme groups

Conference Proceedings of Meetings on Acoustics · December 5, 2022 Cochlear implant (CI) users experience considerable difficulty in understanding speech in reverberant listening environments. This issue is commonly addressed with time-frequency masking, where a time-frequency decomposed reverberant signal is multiplied b ... Full text Cite

Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication.

Conference Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics · October 2022 Brain-computer interfaces (BCIs), such as the P300 speller, can provide a means of communication for individuals with severe neuromuscular limitations. BCIs interpret electroencephalography (EEG) signals in order to translate embedded information about a u ... Full text Cite

Mitigating the Impact of Psychophysical Effects During Adaptive Stimulus Selection in the P300 Speller Brain-Computer Interface.

Conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · November 2021 Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using sensory stimuli to elicit specific neural signal components called event-related potentials (ERPs) to control external devices. However, psychophysical factors, such ... Full text Cite

A causal deep learning framework for classifying phonemes in cochlear implants

Conference ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings · January 1, 2021 Speech intelligibility in cochlear implant (CI) users degrades considerably in listening environments with reverberation and noise. Previous research in automatic speech recognition (ASR) has shown that phoneme-based speech enhancement algorithms improve A ... Full text Cite

Evaluating the effect of longitudinal dose and INR data on maintenance warfarin dose predictions

Conference Bhi 2021 2021 IEEE EMBS International Conference on Biomedical and Health Informatics Proceedings · January 1, 2021 Warfarin, a commonly prescribed drug to prevent blood clots, has a highly variable individual response. Determining a maintenance warfarin dose that achieves a therapeutic blood clotting time, as measured by the international normalized ratio (INR), is cru ... Full text Cite

Heart Sound Analysis in Individuals Supported with Left Ventricular Assist Device: A First Look

Conference Computing in Cardiology · September 13, 2020 Featured Publication The left ventricular assist device (LVAD) has emerged as a bridge or alternative to heart transplant in individuals with advanced heart failure. However, the LVAD recipient population currently faces high rehospitalization rates. Remote analysis of precord ... Full text Cite

USING AUTOMATIC SPEECH RECOGNITION AND SPEECH SYNTHESIS TO IMPROVE THE INTELLIGIBILITY OF COCHLEAR IMPLANT USERS IN REVERBERANT LISTENING ENVIRONMENTS.

Conference Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · May 2020 Cochlear implant (CI) users experience substantial difficulties in understanding reverberant speech. A previous study proposed a strategy that leverages automatic speech recognition (ASR) to recognize reverberant speech and speech synthesis to translate th ... Full text Cite

Automated feature learning using deep convolutional auto-encoder neural network for clustering electroencephalograms into sleep stages

Conference International IEEE EMBS Conference on Neural Engineering Ner · May 16, 2019 Deep neural networks have emerged as popular machine learning tools due to their ability to automatically learn feature representations from raw input data. An auto-encoder neural network is a special network that can be trained in an unsupervised manner f ... Full text Cite

Application of a Graphical Model to Investigate the Utility of Cross-channel Information for Mitigating Reverberation in Cochlear Implants.

Conference Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications · December 2018 Individuals with cochlear implants (CIs) experience more difficulty understanding speech in reverberant environ-ments than normal hearing listeners. As a result, recent research has targeted mitigating the effects of late reverberant signal reflections in ... Full text Cite

Augmented Latent Dirichlet Allocation (Lda) Topic Model with Gaussian Mixture Topics

Conference ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings · September 10, 2018 Latent Dirichlet allocation (LDA) is a statistical model that is often used to discover topics or themes in a large collection of documents. In the LDA model, topics are modeled as discrete distributions over a finite vocabulary of words. The LDA is also a ... Full text Cite

Neurophysiology of Visual-Motor Learning during a Simulated Marksmanship Task in Immersive Virtual Reality

Conference 25th IEEE Conference on Virtual Reality and 3D User Interfaces VR 2018 Proceedings · August 24, 2018 Immersive virtual reality (VR) systems offer flexible control of an interactive environment, along with precise position and orientation tracking of realistic movements. Immersive VR can also be used in conjunction with neurophysiological monitoring techni ... Full text Open Access Cite

USING MACHINE LEARNING TO MITIGATE THE EFFECTS OF REVERBERATION AND NOISE IN COCHLEAR IMPLANTS.

Conference Proceedings of meetings on acoustics. Acoustical Society of America · May 2018 In listening environments with room reverberation and background noise, cochlear implant (CI) users experience substantial difficulties in understanding speech. Because everyday environments have different combinations of reverberation and noise, there is ... Full text Cite

Information-based adaptive stimulus selection to optimize communication efficiency in brain-computer interfaces

Conference Advances in Neural Information Processing Systems · January 1, 2018 Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using a sequence of sensory stimuli to elicit specific neural responses as control signals, while a user attends to relevant target stimuli that occur within the sequence. ... Cite

Adaptive stimulus selection in ERP-based brain-computer interfaces by maximizing expected discrimination gain

Conference 2017 IEEE International Conference on Systems Man and Cybernetics Smc 2017 · November 27, 2017 Brain-computer interfaces (BCIs) can provide an alternative means of communication for individuals with severe neuromuscular limitations. The P300-based BCI speller relies on eliciting and detecting transient event-related potentials (ERPs) in electroencep ... Full text Cite

A performance-based approach to designing the stimulus presentation paradigm for the P300-based BCI by exploiting coding theory

Conference ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings · June 16, 2017 The P300-based brain-computer interface (BCI) speller relies on eliciting and detecting specific brain responses to target stimulus events, termed event-related potentials (ERPs). In a visual speller, ERPs are elicited when the user's desired character, i. ... Full text Cite

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

Conference 54th Annual Allerton Conference on Communication Control and Computing Allerton 2016 · February 10, 2017 The P300 speller is a brain-computer interface that enables people with severe neuromuscular disorders to communicate. It is based on eliciting and detecting event-related potentials (ERP) in electroencephalography (EEG) measurements, in response to rare t ... Full text Cite

Information Theoretic Analysis of the Impact of Refractory Effects on the P300 Speller

Conference The P300 speller is a brain-computer interface that enables people with severe neuromuscular disorders to communicate based on eliciting and detecting event-related potentials (ERP) in electroencephalography (EEG) measurements, in response to rare target s ... Link to item Cite

Information-Theoretic Analysis of Refractory Effects in the P300 Speller

Conference 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 pr ... Link to item Cite