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

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 user's intent into executable commands to control external devices. However, EEG signals are inherently noisy and nonstationary, posing a challenge to extended BCI use. Conventionally, a BCI classifier is trained via supervised learning in an offline calibration session; once trained, the classifier is deployed for online use and is not updated. As the statistics of a user's EEG data change over time, the performance of a static classifier may decline with extended use. It is therefore desirable to automatically adapt the classifier to current data statistics without requiring offline recalibration. In an existing semi-supervised learning approach, the classifier is trained on labeled EEG data and is then updated using incoming unlabeled EEG data and classifier-predicted labels. To reduce the risk of learning from incorrect predictions, a threshold is imposed to exclude unlabeled data with low-confidence label predictions from the expanded training set when retraining the adaptive classifier. In this work, we propose the use of a language model for spelling error correction and disambiguation to provide information about label correctness during semi-supervised learning. Results from simulations with multi-session P300 speller user EEG data demonstrate that our language-guided semi-supervised approach significantly improves spelling accuracy relative to conventional BCI calibration and threshold-based semi-supervised learning.

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

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 as refractory effects and adjacency distractions, may negatively impact ERP elicitation and BCI performance. Although conventional BCI stimulus presentation paradigms usually design stimulus presentation schedules in a pseudo-random manner, recent studies have shown that controlling the stimulus selection process can enhance ERP elicitation. In prior work, we developed an algorithm to adaptively select BCI stimuli using an objective criterion that maximizes the amount of information about the user's intent that can be elicited with the presented stimuli given current data conditions. Here, we enhance this adaptive BCI stimulus selection algorithm to mitigate adjacency distractions and refractory effects by modeling temporal dependencies of ERP elicitation in the objective function and imposing spatial restrictions in the stimulus search space. Results from simulations using synthetic data and human data from a BCI study show that the enhanced adaptive stimulus selection algorithm can improve spelling speeds relative to conventional BCI stimulus presentation paradigms.Clinical relevance-Increased communication rates with our enhanced adaptive stimulus selection algorithm can potentially facilitate the translation of BCIs as viable communication alternatives for individuals with severe neuromuscular limitations.

Novel Acoustic Biomarker of Quality of Life in Left Ventricular Assist Device Recipients.

Background Although technological advances to pump design have improved survival, left ventricular assist device (LVAD) recipients experience variable improvements in quality of life. Methods for optimizing LVAD support to improve quality of life are needed. We investigated whether acoustic signatures obtained from digital stethoscopes can predict patient-centered outcomes in LVAD recipients. Methods and Results We followed precordial sounds over 6 months in 24 LVAD recipients (8 HeartWare HVAD™, 16 HeartMate 3 [HM3]). Subjects recorded their precordial sounds with a digital stethoscope and completed a Kansas City Cardiomyopathy Questionnaire weekly. We developed a novel algorithm to filter LVAD sounds from recordings. Unsupervised clustering of LVAD-mitigated sounds revealed distinct groups of acoustic features. Of 16 HM3 recipients, 6 (38%) had a unique acoustic feature that we have termed the pulse synchronized sound based on its temporal association with the artificial pulse of the HM3. HM3 recipients with the pulse synchronized sound had significantly better Kansas City Cardiomyopathy Questionnaire scores at baseline (median, 89.1 [interquartile range, 86.2-90.4] versus 66.1 [interquartile range, 31.1-73.7]; P=0.03) and over the 6-month study period (marginal mean, 77.6 [95% CI, 66.3-88.9] versus 59.9 [95% CI, 47.9-70.0]; P<0.001). Mechanistically, the pulse synchronized sound shares acoustic features with patient-derived intrinsic sounds. Finally, we developed a machine learning algorithm to automatically detect the pulse synchronized sound within precordial sounds (area under the curve, 0.95, leave-one-subject-out cross-validation). Conclusions We have identified a novel acoustic biomarker associated with better quality of life in HM3 LVAD recipients, which may provide a method for assaying optimized LVAD support.

Heart Sound Analysis in Individuals Supported With Left Ventricular Assist Devices.

OBJECTIVE: LVADs are surgically implanted mechanical pumps that improve survival rates of individuals with advanced heart failure. LVAD therapy is associated with high morbidity, which can be partially attributed to challenges with detecting LVAD complications before adverse events occur. Current methods used to monitor for complications with LVAD support require frequent clinical assessments at specialized LVAD centers. Analysis of recorded precordial sounds may enable real-time, remote monitoring of device and cardiac function for early detection of LVAD complications. The dominance of LVAD sounds in the precordium limits the utility of routine cardiac auscultation of LVAD recipients. In this work, we develop a signal processing pipeline to mitigate sounds generated by the LVAD. METHODS: We collected in vivo precordial sounds from 17 LVAD recipients, and contemporaneous echocardiograms from 12 of these individuals, to validate heart valve closure timings. RESULTS: We characterized various acoustic signatures of heart sounds extracted from in vivo recordings, and report preliminary findings linking fundamental heart sound characteristics and level of LVAD support. CONCLUSION: Mitigation of LVAD sounds from precordial sound recordings of LVAD recipients enables analysis of intrinsic heart sounds. SIGNIFICANCE: These findings provide proof-of-concept evidence of the clinical utility of heart sound analysis for bedside and remote monitoring of LVAD recipients.