Giovanni M. Di Liberto

The paper introduces a framework combining a fine-tuned LLM and TRFs to isolate social context's influence on speech perception, showing social context improves EEG prediction.

This study shows that early sensory input gates phonetic attunement, with visual speech supporting phonetic encoding in the absence of early auditory input.

The paper investigates neural synchronization to linguistic structures and natural speech comprehension, finding that synchronization to phrases and sentences persists despite natural speech anisochrony.

This study applies temporal response functions (TRFs) to fNIRS data during speech perception, showing significant and comparable results to EEG/MEG studies.

This study shows that trust in a speaker affects cortical speech tracking, with lower trust leading to stronger neural tracking of speech.

This study examines how social context affects neural speech tracking using EEG, finding stronger cortical responses to socially relevant speech.

The paper investigates how spontaneous attention influences melody formation during polyphonic music listening using EEG and transformer models, finding that attentional bias shapes neural encoding and aligns with different theoretical models.

This study uses EEG to track neural responses to competing speech streams during attention switches, revealing asymmetric engagement and disengagement processes and lexical context resets.

This paper presents a real-time BCI system using EEG attention decoding to control a hearing instrument, enhancing speech perception in noisy environments by steering attention to desired sound sources.

This study examines how different speech registers, specifically Foreigner-Directed Speech (FDS), affect the cortical encoding of speech in L2 learners and L1 listeners, finding enhanced encoding at multiple processing levels for FDS in L2 learners.

The paper introduces two new metrics to assess neural encoding of lexical surprise in speech comprehension, showing significantly larger effect sizes than existing methods.

The paper replicates previous findings on infant cortical tracking of speech and shows that specific EEG features predict language outcomes at age 1.

This study tracks the development of phonetic feature encoding in infants' cortex from 4 to 11 months using electrophysiological responses, showing increasing acoustically-invariant encoding but not adult-like patterns by 11 months.

This study investigates speech production in children with dyslexia, finding atypical amplitude envelopes in multisyllabic speech but normal pitch contours.

This study investigates how hearing-impaired individuals process ignored speech in noisy environments using EEG, finding over-representation of phonetic onsets in ignored speech.

The paper identifies distinct neural processing patterns in children with dyslexia and Developmental Language Disorder using EEG and speech listening tasks, showing different oscillation dynamics and spatial patterns.

The paper examines delta- and theta-band cortical tracking and phase-amplitude coupling to sung speech in infants, showing their development across 4, 7- and 11-month-olds.

The paper examines how older and younger adults process speech using electrophysiological measures, finding that older adults show reduced semantic pre-activation during language comprehension.

The paper examines how second-language proficiency affects neural encoding of linguistic features in non-native listeners using EEG, finding that proficiency leads to convergence in phonemic encoding with native speakers.

The paper examines how audiovisual speech integration varies with listening conditions, finding enhanced phonetic-level integration in noisy environments.

This study shows that cortical responses to music encode melodic expectations, with neural signals better predicted by combining acoustic and melodic features than either alone.

The paper improves stimulus-response models for brain responses to speech using adaptive techniques and recurrent neural networks, achieving better classification performance in a match-vs-mismatch task.

The paper examines how the brain processes phonotactic probabilities in speech using EEG, finding that these probabilities modulate neural responses and support a pre-lexical role in speech perception.

This paper reviews multiway canonical correlation analysis (MCCA) for fusing brain signals across subjects to extract common components, with examples and discussion of its limitations.

The paper uses EEG and computational models to show that semantic dissimilarity in speech correlates with electrophysiological responses during natural speech comprehension.

This paper introduces Canonical Correlation Analysis (CCA) to decode auditory brain responses and improve stimulus-response mapping for BCI applications.