  The use of Automatic speech recognition (ASR) interfaces have become
increasingly popular in daily life for use in interaction and control of
electronic devices. The interfaces currently being used are not feasible for a
variety of users such as those suffering from a speech disorder, locked-in
syndrome, paralysis or people with utmost privacy requirements. In such cases,
an interface that can identify envisioned speech using electroencephalogram
(EEG) signals can be of great benefit. Various works targeting this problem
have been done in the past. However, there has been limited work in identifying
the frequency bands ($\delta, \theta, \alpha, \beta, \gamma$) of the EEG signal
that contribute towards envisioned speech recognition. Therefore, in this work,
we aim to analyze the significance of different EEG frequency bands and signals
obtained from different lobes of the brain and their contribution towards
recognizing envisioned speech. Signals obtained from different lobes and
bandpass filtered for different frequency bands are fed to a spatio-temporal
deep learning architecture with Convolutional Neural Network (CNN) and Long
Short-Term Memory (LSTM). The performance is evaluated on a publicly available
dataset comprising of three classification tasks - digit, character and images.
We obtain a classification accuracy of $85.93\%$, $87.27\%$ and $87.51\%$ for
the three tasks respectively. The code for the implementation has been made
available at https://github.com/ayushayt/ImaginedSpeechRecognition.
