How training culture normalizes sport bullying: suffering, obedience, and structural violence in athlete development.

Language represents the primary medium for human social interaction, playing a critical role in both information transmission and emotional expression. The frontal lobe of the human brain, serving as the principal module for language processing, provides the neural computing capacity required for the comprehension and generation of language (). Clinical conditions such as stroke, traumatic brain injury, tumors, and amyotrophic lateral sclerosis (ALS) can cause damage to the motor control pathways of the brain, frequently resulting in severe speech impairments, particularly dysarthria (). These impairments significantly limit patients' daily communication and overall quality of life. However, the effective restoration of motor speech functions for individuals with profound speech impairments remains a complex and persistent challenge. Brain-computer interface (BCI) technology establishes a direct communication pathway between the brain and external devices by interpreting electrophysiological signals from the central nervous system. By capturing neural signal fluctuations within the brain regions responsible for speech production and translating them into interpretable communication signals, BCI facilitates the restoration of communicative abilities in patients with speech disorders (; ). This approach is distinct from direct medical intervention, it functions as an assistive technology for the rehabilitation of speech impairments (). Driven by the rapid progression of BCI technologies, a diverse array of neural modalities has been harnessed for speech decoding, including electroencephalography (EEG), functional magnetic resonance imaging (fMRI) (), and electrocorticography (ECoG) (; ). Notably, ) elucidated the complementary relationship between encoding and decoding models within fMRI research. Their findings demonstrate that while both approaches can effectively interrogate neural representations, encoding models provide a more comprehensive functional characterization of brain regions, thereby establishing a rigorous modeling framework that transitions from voxel-wise encoding to stimulus decoding. Similarly, the work of ) confirms the feasibility of semantic category decoding from ECoG signals. By identifying robust high-gamma band activations in language-related cortical regions and analyzing their distinct spatiotemporal dynamics, they validated that machine learning classifiers can reliably predict semantic content from distributed cortical activity, providing a foundational empirical basis for ECoG-driven communication systems.