Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
Physics-informed attention temporal convolutional network for EEG-based motor imagery classification
The brain-computer interface (BCI) is a cutting-edge technology that has the potential to
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …
Machine learning in digital forensics: a systematic literature review
Development and exploitation of technology have led to the further expansion and
complexity of digital crimes. On the other hand, the growing volume of data and …
complexity of digital crimes. On the other hand, the growing volume of data and …
Dynamic convolution with multilevel attention for EEG-based motor imagery decoding
Brain–computer interface (BCI) is an innovative technology that utilizes artificial intelligence
(AI) and wearable electroencephalography (EEG) sensors to decode brain signals and …
(AI) and wearable electroencephalography (EEG) sensors to decode brain signals and …
Shallow and deep feature fusion for digital audio tampering detection
Digital audio tampering detection can be used to verify the authenticity of digital audio.
However, most current methods use standard electronic network frequency (ENF) databases …
However, most current methods use standard electronic network frequency (ENF) databases …
Spoken language identification system using convolutional recurrent neural network
Following recent advancements in deep learning and artificial intelligence, spoken
language identification applications are playing an increasingly significant role in our day-to …
language identification applications are playing an increasingly significant role in our day-to …
Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques
In the era of automated and digitalized information, advanced computer applications deal
with a major part of the data that comprises audio-related information. Advancements in …
with a major part of the data that comprises audio-related information. Advancements in …
Audio tampering forensics based on representation learning of enf phase sequence
This paper proposes an audio tampering detection method based on the ENF phase and BI-
LSTM network from the perspective of temporal feature representation learning. First, the …
LSTM network from the perspective of temporal feature representation learning. First, the …
Modern standard Arabic speech corpora: a systematic review
Speech processing applications have become integral components across various domains
of modern life. The design and preparation of a reliable recognition system rely heavily on …
of modern life. The design and preparation of a reliable recognition system rely heavily on …
Crossmixed convolutional neural network for digital speech recognition
Digital speech recognition is a challenging problem that requires the ability to learn complex
signal characteristics such as frequency, pitch, intensity, timbre, and melody, which …
signal characteristics such as frequency, pitch, intensity, timbre, and melody, which …