  Brain Signals, such as Electroencephalography (EEG), and human languages have
been widely explored independently for many downstream tasks, however, the
connection between them has not been well explored. In this study, we explore
the relationship and dependency between EEG and language. To study at the
representation level, we introduced \textbf{MTAM}, a \textbf{M}ultimodal
\textbf{T}ransformer \textbf{A}lignment \textbf{M}odel, to observe coordinated
representations between the two modalities. We used various relationship
alignment-seeking techniques, such as Canonical Correlation Analysis and
Wasserstein Distance, as loss functions to transfigure features. On downstream
applications, sentiment analysis and relation detection, we achieved new
state-of-the-art results on two datasets, ZuCo and K-EmoCon. Our method
achieved an F1-score improvement of 1.7% on K-EmoCon and 9.3% on Zuco datasets
for sentiment analysis, and 7.4% on ZuCo for relation detection. In addition,
we provide interpretations of the performance improvement: (1) feature
distribution shows the effectiveness of the alignment module for discovering
and encoding the relationship between EEG and language; (2) alignment weights
show the influence of different language semantics as well as EEG frequency
features; (3) brain topographical maps provide an intuitive demonstration of
the connectivity in the brain regions. Our code is available at
\url{https://github.com/Jason-Qiu/EEG_Language_Alignment}.
