Biology subjects

Haro, S.

Publications and source records attributed to Haro, S..

5 recordsLinked to original sources

bioRxiv2025

Decoding intended speech with an intracortical brain-computer interface in a person with longstanding anarthria and locked-in syndrome

Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with longstanding anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher-order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we are able to attain an extensive characterization of the neural signals underlying speech in a person with locked-in syndrome and through our results identify several directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus. Overall, these results demonstrate that speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence. For individuals with longstanding anarthria, a purely phoneme-based decoding approach may lack the accuracy necessary to support independent use as a primary means of communication; however, additional linguistic information embedded within neural signals may provide a route to augment the performance of speech decoders.

neuroscience↗

bioRxiv2025

A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments

ObjectiveThere is significant research in accurately determining the focus of a listeners attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listeners focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listeners attention to the target speaker in real time and investigate the underlying neural bases of this improvement. ApproachThis paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listeners real-time attention decoding accuracy was used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding. ResultsIn this study, we found evidence of suppression of (i.e., reduction in) neural tracking of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.012). We did not find a statistically significant increase in the neural tracking of the attended talker. SignificanceThese results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.

neuroscience↗

bioRxiv2021

EEG Alpha Power and Pupil Diameter Reflect Endogenous Auditory Attention Switching and Listening Effort

Auditory attention describes a listeners focus on an acoustic source while they ignore other competing sources that might be present. In an environment with multiple talkers and background noise (i.e. the cocktail party effect), auditory attention can be difficult, requiring the listener to expend measurable cognitive effort. A listener will naturally interrupt sustained attention on a source when switching towards another source during conversation. This change in attention is potentially even more taxing than maintaining sustained attention due to the limits of human working memory, and this additional effort required has not been well studied. In this work, we evaluated an attention decoder algorithm for detecting the change in attention and investigated cognitive effort expended during attentional switching and sustained attention. Two variants of endogenous attention switching were explored: the switches either had in-the-moment decision making or a pre-defined attentional switch time. A least-squares, EEG-based, attention decoding algorithm achieved 64.1% accuracy with a 5-second correlation window and illustrated smooth transitions in the attended talker prediction through switches in sustained attention at approximately half of the analysis window size (2.2 seconds). The expended listening effort, as measured by simultaneous electroencephalography (EEG) and pupillometry, was also a strong indicator of switching. Specifically, centrotemporal alpha power [F(2, 18) = 7.473, P = 0.00434] and mean pupil diameter [F(2, 18) = 9.159, P = 0.0018] were significantly different for trials that contained a switch in comparison to sustained trials. We also found that relative attended and ignored talker locations modulate the EEG alpha topographic response. This alpha lateralization was found to be impacted by the interaction between experimental condition and whether the measure was computed before or after the switch [F(2,18) = 3.227, P = 0.0634]. These results suggest that expended listening effort is a promising feature that should be pursued in a decoding context, in addition to speech and location-based features.

neuroscience↗

bioRxiv2020

A comprehensive computational model of animal biosonar signal processing

Computational models of animal biosonar seek to identify critical aspects of echo processing responsible for the superior, real-time performance of echolocating bats and dolphins in target tracking and clutter rejection. The Spectrogram Correlation and Transformation (SCAT) model replicates aspects of biosonar imaging in both species by processing wideband biosonar sounds and echoes with auditory mechanisms identified from experiments with bats. The model acquires broadband biosonar broadcasts and echoes, represents them as time-frequency spectrograms using parallel bandpass filters, translates the filtered signals into ten parallel amplitude threshold levels, and then operates on the resulting time-of-occurrence values at each frequency to estimate overall echo range delay. It uses the structure of the echo spectrum by depicting it as a series of local frequency nulls arranged regularly along the frequency axis of the spectrograms after dechirping them relative to the broadcast. Computations take place entirely on the timing of threshold-crossing events for each echo relative to threshold-events for the broadcast. Threshold-crossing times take into account amplitude-latency trading, a physiological feature absent from conventional digital signal processing. Amplitude-latency trading transposes the profile of amplitudes across frequencies into a profile of time-registrations across frequencies. Target shape is extracted from the spacing of the objects individual acoustic reflecting points, or glints, using the mutual interference pattern of peaks and nulls in the echo spectrum. These are merged with the overall range-delay estimate to produce a delay-based reconstruction of the objects distance as well as its glints. Clutter echoes indiscriminately activate multiple parts in the null-detecting system, which then produces the equivalent glint-delay spacings in images, thus blurring the overall echo-delay estimates by adding spurious glint delays to the image. Blurring acts as an anticorrelation process that rejects clutter intrusion into perceptions. Author summaryBats and dolphins use their biological sonar as a versatile, high-resolution perceptual system that performs at levels desirable in man-made sonar or radar systems. To capture the superior real-time capabilities of biosonar so they can be imported into the design of new man-made systems, we developed a computer model of the sonar receiver used by echolocating bats and dolphins. Our intention was to discover the processing methods responsible for the animals ability to find and identify targets, guide locomotion, and prevent classic types of sonar or radar interference that hamper performance of man-made systems in complex, rapidly-changing surroundings. We have identified several features of the ears, hearing, time-frequency representation, and auditory processing that are critical for organizing echo-processing methods and display manifested in the animals perceptions.

neuroscience↗

bioRxiv2018

Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods

Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which should be enhanced for the listener and which should be suppressed. Traditionally, researchers have separated the AAD problem into two stages: reconstruction of a representation of the attended audio from neural signals, followed by determining the similarity between the candidate audio streams and the reconstruction. In this work, we compare the traditional two-stage approach with a novel neural-network architecture that subsumes the explicit similarity step. We compare this new architecture against linear and non-linear (neural-network) baselines using both wet and dry electroencephalogram (EEG) systems. Our results indicate that the wet and dry systems can deliver comparable results despite the latter having one third as many EEG channels as the former, and that the new architecture outperforms the baseline stimulus-reconstruction methods for both EEG modalities. The 14-subject, wet-electrode AAD dataset for two competing, co-located talkers, the 11-subject, dry-electrode AAD dataset, and our software are available to download for further validation, experimentation, and modification.

neuroscience↗