  We present a new approach, that we call AdaGTCN, for identifying human reader
intent from Electroencephalogram~(EEG) and Eye movement~(EM) data in order to
help differentiate between normal reading and task-oriented reading.
Understanding the physiological aspects of the reading process~(the cognitive
load and the reading intent) can help improve the quality of crowd-sourced
annotated data. Our method, Adaptive Graph Temporal Convolution Network
(AdaGTCN), uses an Adaptive Graph Learning Layer and Deep Neighborhood Graph
Convolution Layer for identifying the reading activities using time-locked EEG
sequences recorded during word-level eye-movement fixations. Adaptive Graph
Learning Layer dynamically learns the spatial correlations between the EEG
electrode signals while the Deep Neighborhood Graph Convolution Layer exploits
temporal features from a dense graph neighborhood to establish the state of the
art in reading task identification over other contemporary approaches. We
compare our approach with several baselines to report an improvement of 6.29%
on the ZuCo 2.0 dataset, along with extensive ablation experiments
