Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Human identification with electroencephalogram (EEG) signal processing
person identification from the eeg using nonlinear signal classification .
Artifact detection in EEG using machine learning
Resting State EEG-based biometrics for individual identification using convolutional neural networks
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification
Survey on EEG Signal Processing Methods
In-Ear EEG Biometrics for Feasible and Readily Collectable Real-World Person Authentication
Classification of EEG signals using neural network and logistic regression
Real-time detection of brain events in EEG
Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface
REAL-TIME EEG BASED OBJECT RECOGNITION
Imaging human EEG dynamics using independent component analysis
Emotion recognition from EEG signals by using multivariate empirical mode decomposition
An EEG artifact identification embedded system using ICA and multi-instance learning
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques
Analysis of EEG Signal Processing Techniques based on Spectrograms
Comparison of linear, nonlinear, and feature selection methods for EEG signal classification
EEG Signal Classification Using Power Spectral Features and linear Discriminant Analysis: A Brain Computer Interface Application
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
Real-Time EEG-Based Human Emotion Recognition and Visualization
Classification of human emotion from EEG using discrete wavelet transform
A general metric for identifying adversarial images
Evidence-based postoperative pain management after laparoscopic colorectal surgery
Iris recognition: an emerging biometric technology
An effective SINR mapping models for 256QAM in LTE-Advanced system
Research on the Forecast of Shared Bicycle Rental Demand Based on Spark Machine Learning Framework