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2. Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework

Author

Nouri, Mahbod, Moradi, Faraz, Ghaemi, Hafez, and Nasrabadi, Ali Motie

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Signal Processing, I.5.1, I.5.4
Abstract

A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been developed. Many of these methods yield a weaker performance compared to the subject-dependent (SD) approach, and some are computationally expensive. A potential real-world application would greatly benefit from a more accurate, compact, and computationally efficient subject-independent BCI. In this work, we propose a novel subject-independent BCI framework, named CCSPNet (Convolutional Common Spatial Pattern Network) that is trained on the motor imagery (MI) paradigm of a large-scale electroencephalography (EEG) signals database consisting of 400 trials for every 54 subjects who perform two-class hand-movement MI tasks. The proposed framework applies a wavelet kernel convolutional neural network (WKCNN) and a temporal convolutional neural network (TCNN) in order to represent and extract the spectral features of EEG signals. A common spatial pattern (CSP) algorithm is implemented for spatial feature extraction, and the number of CSP features is reduced by a dense neural network. Finally, the class label is determined by a linear discriminant analysis (LDA) classifier. The CCSPNet evaluation results show that it is possible to have a compact BCI that achieves both SD and SI state-of-the-art performance comparable to complex and computationally expensive models., Comment: 27 pages, 6 figures, 8 tables, 1 algorithm

Published
2020

11. Optimized Mother Wavelet in a Combination of Wavelet Packet with Detrended Fluctuation Analysis for Controlling a Remote Vehicle with Imagery Movement: A Brain Computer Interface Study

Author

Hekmatmanesh, Amin, Wu, Huapeng, Li, Ming, Nasrabadi, Ali Motie, Handroos, Heikki, Ceccarelli, Marco, Series Editor, Hernandez, Alfonso, Editorial Board Member, Huang, Tian, Editorial Board Member, Velinsky, Steven A., Editorial Board Member, Takeda, Yukio, Editorial Board Member, Corves, Burkhard, Editorial Board Member, Carbone, Giuseppe, editor, and Pisla, Doina, editor

Published
2019
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15. The Effectiveness of High-Definition Transcranial Direct Current Stimulation (HD-tDCS) over the Dorsolateral Prefrontal Cortex (DLPFC) in Improving Induced Emotion Dysregulation and Information Processing Speed.

Author

AmelRahbardar, AmirHossein, Hatef, Boshra, Nasrabadi, Ali Motie, Meftahi, Gholam Hossein, and Mohammadi, Alireza

Subjects
BRAIN physiology, EMOTION regulation, PSYCHOLOGICAL distress, PREFRONTAL cortex, COGNITIVE processing speed, STATISTICAL sampling, QUESTIONNAIRES, ANXIETY, DEEP brain stimulation, DESCRIPTIVE statistics, PRE-tests & post-tests, AGGRESSION (Psychology), IMPULSIVE personality, NEUROPSYCHOLOGICAL tests, TRANSCRANIAL direct current stimulation, COMPARATIVE studies, WELL-being, COGNITION
Abstract

Background and Aim: Emotion regulation is an important and determining factor in psychological wellbeing and effective behavior of people, which plays an essential role in adapting to stressful life events. People who experience emotion dysregulation may show symptoms such as impulsive behaviors, severe anxiety, aggression, and problems with metacognition. This study aims to investigate the effectiveness of high-definition transcranial direct current stimulation (HD-tDCS) in the dorsolateral prefrontal cortex (DLPFC) of the brain in improving emotion dysregulation and the speed of brain information processing. Methods: This study is pre-post-test research. In this study, 20 healthy participants (male, right-handed) were used. The participants were randomly assigned to two anodal stimulation groups and sham (control) groups. By completing the ERQ and DTS questionnaires, as well as performing the Paced Auditory Serial Addition Test (PASAT) before and after electrical stimulation of the brain by the HD-tDCS method in the L-DLPFC area, this study was followed up and carried out. Results: After the intervention (stimulation) in the pre-post test comparison between the stimulation group and the control group, there was a difference in terms of evaluation variables, suppression, being absorbed by negative emotions, regulating efforts to relieve distress, the number of correct answers in the PASAT test and the time to answer the PASAT test. There was significance (P<0.05). This shows the effectiveness of the intervention (anodal stimulation) on these components between the stimulation and control groups. However, there was no significant difference between the stimulation and control groups in terms of emotional distress tolerance, subjective estimation of distress, and the number of incorrect answers in the PASAT test (P>0.05). Also, in the comparison of the pre-post test, time was not recognized as a significant factor in any of the research components. Conclusion: High-definition transcranial direct current stimulation (HD-tDCS) in the left dorsolateral prefrontal cortex (DLPFC) improves some components of emotion dysregulation and also improves brain information processing speed. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Epileptic seizure prediction based on features extracted from lagged Poincaré plots.

Author

Behbahani, Soroor, Jafarnia Dabanloo, Nader, Nasrabadi, Ali Motie, and Dourado, Antonio

Subjects
EPILEPSY, HEART beat, FEATURE extraction, PEOPLE with epilepsy, FORECASTING
Abstract

The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR). In this article, the Poincaré plots with six different lags (1–6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points. The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor. The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Recurrence Plots for Identifying Memory Components in Single-Trial EEGs

Author

Talebi, Nasibeh, Nasrabadi, Ali Motie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Yao, Yiyu, editor, Sun, Ron, editor, Poggio, Tomaso, editor, Liu, Jiming, editor, Zhong, Ning, editor, and Huang, Jimmy, editor

Published
2010
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39. Towards real-world BCI: CCSPNet, a compact subject-independent motor imagery framework

Author

Nouri, Mahbod, Moradi, Faraz, Ghaemi, Hafez, and Nasrabadi, Ali Motie

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, I.5.1, Computer Science - Artificial Intelligence, I.5.4, Applied Mathematics, Computer Science - Neural and Evolutionary Computing, Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Computational Theory and Mathematics, Artificial Intelligence, Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering, Neural and Evolutionary Computing (cs.NE), Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Signal Processing, Electrical and Electronic Engineering, Statistics, Probability and Uncertainty
Abstract

A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been developed. Many of these methods yield a weaker performance compared to the subject-dependent (SD) approach, and some are computationally expensive. A potential real-world application would greatly benefit from a more accurate, compact, and computationally efficient subject-independent BCI. In this work, we propose a novel subject-independent BCI framework, named CCSPNet (Convolutional Common Spatial Pattern Network) that is trained on the motor imagery (MI) paradigm of a large-scale electroencephalography (EEG) signals database consisting of 400 trials for every 54 subjects who perform two-class hand-movement MI tasks. The proposed framework applies a wavelet kernel convolutional neural network (WKCNN) and a temporal convolutional neural network (TCNN) in order to represent and extract the spectral features of EEG signals. A common spatial pattern (CSP) algorithm is implemented for spatial feature extraction, and the number of CSP features is reduced by a dense neural network. Finally, the class label is determined by a linear discriminant analysis (LDA) classifier. The CCSPNet evaluation results show that it is possible to have a compact BCI that achieves both SD and SI state-of-the-art performance comparable to complex and computationally expensive models., Comment: 27 pages, 6 figures, 8 tables, 1 algorithm

Published
2023

43. The 2017 and 2018 Iranian Brain–Computer interface competitions

Author

Aghdam, Nasser Samadzadeh, Moradi, Mohammad Hassan, Shamsollahi, Mohammad Bagher, Nasrabadi, Ali Motie, Setarehdan, Seyed Kamaledin, Shalchyan, Vahid, Faradji, Farhad, and Makkiabadi, Bahador

Subjects
motor imagery, lcsh:Medical technology, lcsh:R855-855.5, brain–computer interface, movement onset, Meeting Report, electroencephalography, motor execution
Abstract

This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms and discuss the organizational strategies for future campaigns.

Published
2020

50. First Event-Related Potentials Evidence of Auditory Morphosyntactic Processing in a Subject-Object-Verb Nominative-Accusative Language (Farsi)

Author

Meykadeh, Azam, Golfam, Arsalan, Nasrabadi, Ali Motie, Ameri, Hayat, Sommer, Werner, Meykadeh, Azam, Golfam, Arsalan, Nasrabadi, Ali Motie, Ameri, Hayat, and Sommer, Werner

Abstract

While most studies on neural signals of online language processing have focused on a few—usually western—subject-verb-object (SVO) languages, corresponding knowledge on subject-object-verb (SOV) languages is scarce. Here we studied Farsi, a language with canonical SOV word order. Because we were interested in the consequences of second-language acquisition, we compared monolingual native Farsi speakers and equally proficient bilinguals who had learned Farsi only after entering primary school. We analyzed event-related potentials (ERPs) to correct and morphosyntactically incorrect sentence-final syllables in a sentence correctness judgment task. Incorrect syllables elicited a late posterior positivity at 500–700 ms after the final syllable, resembling the P600 component, as previously observed for syntactic violations at sentence-middle positions in SVO languages. There was no sign of a left anterior negativity (LAN) preceding the P600. Additionally, we provide evidence for a real-time discrimination of phonological categories associated with morphosyntactic manipulations (between 35 and 135 ms), manifesting the instantaneous neural response to unexpected perturbations. The L2 Farsi speakers were indistinguishable from L1 speakers in terms of performance and neural signals of syntactic violations, indicating that exposure to a second language at school entry may results in native-like performance and neural correlates. In nonnative (but not native) speakers verbal working memory capacity correlated with the late posterior positivity and performance accuracy. Hence, this first ERP study of morphosyntactic violations in a spoken SOV nominative-accusative language demonstrates ERP effects in response to morphosyntactic violations and the involvement of executive functions in non-native speakers in computations of subject-verb agreement., Peer Reviewed

Published
2021

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