Volume 17 (2023)
Volume 16 (2022)
Volume 15 (2021)
Volume 14 (2020)
Volume 13 (2019)
Volume 12 (2018)
Volume 11 (2017)
Volume 10 (2016)
Volume 9 (2015)
Volume 8 (2014)
Volume 7 (2013)
Volume 6 (2012)
Volume 5 (2011)
Volume 4 (2010)
Volume 3 (2009)
Volume 2 (2008)
Volume 1 (2007)
Volume -2 (2005)
Volume -1 (2004)
Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Combined Method of EMD with CCA or LASSO to Detect SSVEP Frequency

Marzie Alirezaei Alavijeh; Ali Maleki

Volume 16, Issue 1 , May 2022, , Pages 1-9

  Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such ...  Read More

EEG-Based Emotional State Recognition using Deep Learning Network

Seyedeh Saeideh Zahedi Haghighi; Sayed Mahmoud Sakhaei; Mohammadreza Daliri

Volume 13, Issue 2 , August 2019, , Pages 95-104

  Emotion is one of the most important human quality that plays an important role in life. Also, one way of communicating between human and computer is based on emotion recognition. One way of emotion recognition is based on electroencephalographic signal (EEG). In this paper, according to the non-stationary ...  Read More

Human Computer Interaction / HCI
The EMD-CCA with Neural Network Classifier to Recognize the SSVEP Frequency

Sahar Sadeghi; Ali Maleki

Volume 11, Issue 2 , June 2017, , Pages 101-109

  To increase the number of stimulation frequencies in the Steady-state visual evoked potential (SSVEP)-based brain-computer interface, we are forced to broaden the frequency range due to the frequency resolution restriction. This will enter frequencies with harmonic relation into the stimulation frequency ...  Read More

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Empirical mode decomposition-based elimination of Electrocardiogram artifact from Electromyogram signals

Mohsen Naji; Seyed Mohammad Firouzabadi; Sedighe Kahrizi

Volume 7, Issue 1 , June 2013, , Pages 13-20

  The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from ...  Read More