Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques

Vaibhav Gandhi, G Prasad, DH Coyle, Laxmidhar Behera, TM McGinnity

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

The raw EEG signal acquired non-invasively from the sensorimotor cortex during the motor imagery (MI) performed by a brain-computer interface (BCI) user is naturally embedded with noise while the actual noise-free EEG is still unattainable. This paper compares the enhancement in information when filtering these noisy EEG signals while using a Schrodinger wave equation (SWE) based Recurrent QuantumNeural Network (RQNN) model and a Savitzky–Golay (SG) filtering model, while investigating over multiple classification techniques on several datasets. The RQNN model is designed to efficiently capture the statistical behavior of the input signal using an unsupervised learning scheme. The algorithm is robust to parametric sensitivity and does not make any a priori assumption about the true signal type or the embedded noise. The performance of both the filtering approaches, investigated for the BCI competition IV 2b dataset as well as the off-line datasets on subjects in the BCI laboratory, over multiple classifiers shows that the RQNN can potentially be a flexible technique that can suit different classifiers for real-time EEG signal filtering. The average classification accuracy performance across all the subjects with the RQNN technique is better than the SG (and the unfiltered signal) by approximately 5% (and 7%) and 1% (and 4%) during the training and the evaluation stages respectively.
LanguageEnglish
Pages161-167
JournalNeurocomputing
Volume170
DOIs
Publication statusPublished - 17 Jul 2015

Fingerprint

Brain computer interface
Electroencephalography
Neural networks
Classifiers
Information filtering
Unsupervised learning
Wave equations

Keywords

  • Recurrent Quantum Neural Network (RQNN)
  • Brain-computer interface (BCI)
  • EEG Filtering.

Cite this

@article{02933421d3ce4adb847b3f7c002c5406,
title = "Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques",
abstract = "The raw EEG signal acquired non-invasively from the sensorimotor cortex during the motor imagery (MI) performed by a brain-computer interface (BCI) user is naturally embedded with noise while the actual noise-free EEG is still unattainable. This paper compares the enhancement in information when filtering these noisy EEG signals while using a Schrodinger wave equation (SWE) based Recurrent QuantumNeural Network (RQNN) model and a Savitzky–Golay (SG) filtering model, while investigating over multiple classification techniques on several datasets. The RQNN model is designed to efficiently capture the statistical behavior of the input signal using an unsupervised learning scheme. The algorithm is robust to parametric sensitivity and does not make any a priori assumption about the true signal type or the embedded noise. The performance of both the filtering approaches, investigated for the BCI competition IV 2b dataset as well as the off-line datasets on subjects in the BCI laboratory, over multiple classifiers shows that the RQNN can potentially be a flexible technique that can suit different classifiers for real-time EEG signal filtering. The average classification accuracy performance across all the subjects with the RQNN technique is better than the SG (and the unfiltered signal) by approximately 5{\%} (and 7{\%}) and 1{\%} (and 4{\%}) during the training and the evaluation stages respectively.",
keywords = "Recurrent Quantum Neural Network (RQNN), Brain-computer interface (BCI), EEG Filtering.",
author = "Vaibhav Gandhi and G Prasad and DH Coyle and Laxmidhar Behera and TM McGinnity",
year = "2015",
month = "7",
day = "17",
doi = "10.1016/j.neucom.2014.12.114",
language = "English",
volume = "170",
pages = "161--167",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",

}

Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques. / Gandhi, Vaibhav; Prasad, G; Coyle, DH; Behera, Laxmidhar; McGinnity, TM.

In: Neurocomputing, Vol. 170, 17.07.2015, p. 161-167.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Evaluating Quantum Neural Network filtered motor imagery brain-computer interface using multiple classification techniques

AU - Gandhi, Vaibhav

AU - Prasad, G

AU - Coyle, DH

AU - Behera, Laxmidhar

AU - McGinnity, TM

PY - 2015/7/17

Y1 - 2015/7/17

N2 - The raw EEG signal acquired non-invasively from the sensorimotor cortex during the motor imagery (MI) performed by a brain-computer interface (BCI) user is naturally embedded with noise while the actual noise-free EEG is still unattainable. This paper compares the enhancement in information when filtering these noisy EEG signals while using a Schrodinger wave equation (SWE) based Recurrent QuantumNeural Network (RQNN) model and a Savitzky–Golay (SG) filtering model, while investigating over multiple classification techniques on several datasets. The RQNN model is designed to efficiently capture the statistical behavior of the input signal using an unsupervised learning scheme. The algorithm is robust to parametric sensitivity and does not make any a priori assumption about the true signal type or the embedded noise. The performance of both the filtering approaches, investigated for the BCI competition IV 2b dataset as well as the off-line datasets on subjects in the BCI laboratory, over multiple classifiers shows that the RQNN can potentially be a flexible technique that can suit different classifiers for real-time EEG signal filtering. The average classification accuracy performance across all the subjects with the RQNN technique is better than the SG (and the unfiltered signal) by approximately 5% (and 7%) and 1% (and 4%) during the training and the evaluation stages respectively.

AB - The raw EEG signal acquired non-invasively from the sensorimotor cortex during the motor imagery (MI) performed by a brain-computer interface (BCI) user is naturally embedded with noise while the actual noise-free EEG is still unattainable. This paper compares the enhancement in information when filtering these noisy EEG signals while using a Schrodinger wave equation (SWE) based Recurrent QuantumNeural Network (RQNN) model and a Savitzky–Golay (SG) filtering model, while investigating over multiple classification techniques on several datasets. The RQNN model is designed to efficiently capture the statistical behavior of the input signal using an unsupervised learning scheme. The algorithm is robust to parametric sensitivity and does not make any a priori assumption about the true signal type or the embedded noise. The performance of both the filtering approaches, investigated for the BCI competition IV 2b dataset as well as the off-line datasets on subjects in the BCI laboratory, over multiple classifiers shows that the RQNN can potentially be a flexible technique that can suit different classifiers for real-time EEG signal filtering. The average classification accuracy performance across all the subjects with the RQNN technique is better than the SG (and the unfiltered signal) by approximately 5% (and 7%) and 1% (and 4%) during the training and the evaluation stages respectively.

KW - Recurrent Quantum Neural Network (RQNN)

KW - Brain-computer interface (BCI)

KW - EEG Filtering.

U2 - 10.1016/j.neucom.2014.12.114

DO - 10.1016/j.neucom.2014.12.114

M3 - Article

VL - 170

SP - 161

EP - 167

JO - Neurocomputing

T2 - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -