arXiv subjects

Mike Thornton

Publications and source records attributed to Mike Thornton.

5 recordsLinked to original sources

arXiv2024

Detecting gamma-band responses to the speech envelope for the ICASSP 2024 Auditory EEG Decoding Signal Processing Grand Challenge

The 2024 ICASSP Auditory EEG Signal Processing Grand Challenge concerns the decoding of electroencephalography (EEG) measurements taken from participants who listened to speech material. This work details our solution to the match-mismatch sub-task: given a short temporal segment of EEG recordings and several candidate speech segments, the task is to classify which of the speech segments was time-aligned with the EEG signals. We show that high-frequency gamma-band responses to the speech envelope can be detected with a high accuracy. By jointly assessing gamma-band responses and low-frequency envelope tracking, we develop a match-mismatch decoder which placed first in this task.

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arXiv2024

Comparison of linear and nonlinear methods for decoding selective attention to speech from ear-EEG recordings

Many people with hearing loss struggle to comprehend speech in crowded auditory scenes, even when they are using hearing aids. It has recently been demonstrated that the focus of a listener's selective attention to speech can be decoded from their electroencephalography (EEG) recordings, raising the prospect of smart EEG-steered hearing aids which restore speech comprehension in adverse acoustic environments (such as the cocktail party). To this end, we here assess the feasibility of using a novel, ultra-wearable ear-EEG device to classify the selective attention of normal-hearing listeners who participated in a two-talker competing-speakers experiment. Eighteen participants took part in a diotic listening task, whereby they were asked to attend to one narrator whilst ignoring the other. Encoding models were estimated from the recorded signals, and these confirmed that the device has the ability to capture auditory responses that are consistent with those reported in high-density EEG studies. Several state-of-the-art auditory attention decoding algorithms were next compared, including stimulus-reconstruction algorithms based on linear regression as well as non-linear deep neural networks, and canonical correlation analysis (CCA). Meaningful markers of selective auditory attention could be extracted from the ear-EEG signals of all 18 participants, even when those markers were derived from relatively short EEG segments of just five seconds in duration. Algorithms which related the EEG signals to the rising edges of the speech temporal envelope (onset envelope) were more successful than those which made use of the temporal envelope itself. The CCA algorithm achieved the highest mean attention decoding accuracy, although differences between the performances of the three algorithms were both small and not statistically significant when EEG segments of short durations were employed.

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arXiv2023

Decoding Envelope and Frequency-Following EEG Responses to Continuous Speech Using Deep Neural Networks

The electroencephalogram (EEG) offers a non-invasive means by which a listener's auditory system may be monitored during continuous speech perception. Reliable auditory-EEG decoders could facilitate the objective diagnosis of hearing disorders, or find applications in cognitively-steered hearing aids. Previously, we developed decoders for the ICASSP Auditory EEG Signal Processing Grand Challenge (SPGC). These decoders aimed to solve the match-mismatch task: given a short temporal segment of EEG recordings, and two candidate speech segments, the task is to identify which of the two speech segments is temporally aligned, or matched, with the EEG segment. The decoders made use of cortical responses to the speech envelope, as well as speech-related frequency-following responses, to relate the EEG recordings to the speech stimuli. Here we comprehensively document the methods by which the decoders were developed. We extend our previous analysis by exploring the association between speaker characteristics (pitch and sex) and classification accuracy, and provide a full statistical analysis of the final performance of the decoders as evaluated on a heldout portion of the dataset. Finally, the generalisation capabilities of the decoders are characterised, by evaluating them using an entirely different dataset which contains EEG recorded under a variety of speech-listening conditions. The results show that the match-mismatch decoders achieve accurate and robust classification accuracies, and they can even serve as auditory attention decoders without additional training.

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arXiv2023

Relating EEG recordings to speech using envelope tracking and the speech-FFR

During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG recordings is challenging due to the low signal-to-noise ratios of EEG signals. We report on an approach developed for the ICASSP 2023 'Auditory EEG Decoding' Signal Processing Grand Challenge. A simple ensembling method is shown to considerably improve upon the baseline decoder performance. Even higher classification rates are achieved by jointly decoding the speech-evoked frequency-following response and responses to the temporal envelope of speech, as well as by fine-tuning the decoders to individual subjects. Our results could have applications in the diagnosis of hearing disorders or in cognitively steered hearing aids.

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arXiv2022

Ear-EEG Sensitivity Modelling for Neural and Artifact Sources

The ear-EEG has emerged as a promising candidate for wearable brain monitoring in real-world scenarios. While experimental studies have validated ear-EEG in multiple scenarios, the source-sensor relationship for a variety of neural sources has not been established. In addition, a detailed theoretical analysis of the ear-EEG sensitivity to sources of artifacts is still missing. Within the present study, the sensitivity of various configurations of ear-EEG is established in the presence of neural sources from a range of brain surface locations, in addition to ocular sources for the blink, vertical saccade, and horizontal saccade eye movements which produce artifacts in the EEG signal. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG for sources that are located further away from the ears is supported through the analysis of the prominence of ocular artifacts in ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of both neural and ocular artifact sensitivity.

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