Deciphering the intricate neural mechanisms behind human speech perception mandates the seamless alignment of auditory signals with electroencephalogram (EEG) data. Yet, the unpredictable fluctuations and inherent noise within EEG, intertwined with the delicate dance between sound and neural response, cast a daunting challenge before researchers. Within the realm of this challenge, our study presents a groundbreaking iteration of the Conformer architecture, meticulously fine-tuned for the exacting task of audio-EEG matching.Where the conventional Conformer pivots on a self-attention mechanism, our enhanced version echoes a more nuanced tune, introducing what we’ve termed "local attention." This deliberate design choice emphasizes nearby inputs, capturing those elusive local patterns and relationships pivotal to understanding the spatial interplay between auditory and EEG features. Such a precision-focused approach guides the model to spotlight vital data sectors during its training voyage, unearthing features rich in meaning and contextual relevance.Our empirical symphony resonates with the brilliance of this enhanced Conformer. It consistently surpasses traditional methods, echoing superior prowess in audio-EEG matching accuracy, and in turn, amplifying the resonance of our innovative approach.