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PubMed Publications| Keyword search (4,163 papers available) |  |
"Mohammadi A" Authored Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module | Khademi S; Heidarian S; Afshar P; Mohammadi A; Sidiqi A; Nguyen ET; Ganeshan B; Oikonomou A; | 41150036 ENCS |
| 2 | Energy-delay analysis in advection-diffusion-based wireless body area networks | Kianfar G; Hosseini P; Azadi M; Abouei J; Mohammadi A; | 40880450 ENCS |
| 3 | Differentiation of COVID-19 from other types of viral pneumonia and severity scoring on baseline chest radiographs: Comparison of deep learning with multi-reader evaluation | Enshaei N; Mohammadi A; Naderkhani F; Daneman N; Abu Mughli R; Anconina R; Berger FH; Kozak RA; Mubareka S; Villanueva Campos AM; Narang K; Vivekanandan T; Chan AK; Lam P; Andany N; Oikonomou A; | 40729327 ENCS |
| 4 | Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals | Montazerin M; Rahimian E; Naderkhani F; Atashzar SF; Yanushkevich S; Mohammadi A; | 37419881 ENCS |
| 5 | Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans | Khademi S; Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Oikonomou A; Shafiee A; Babaki Fard F; Plataniotis KN; Mohammadi A; | 36862633 ENCS |
| 6 | DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis | Karimi R; Mohammadi A; Asif A; Benali H; | 35408182 ENCS |
| 7 | Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network | Afshar P; Rafiee MJ; Naderkhani F; Heidarian S; Enshaei N; Oikonomou A; Babaki Fard F; Anconina R; Farahani K; Plataniotis KN; Mohammadi A; | 35318368 ENCS |
| 8 | COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images | Enshaei N; Oikonomou A; Rafiee MJ; Afshar P; Heidarian S; Mohammadi A; Plataniotis KN; Naderkhani F; | 35217712 ENCS |
| 9 | COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans | Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Babaki Fard F; Samimi K; Atashzar SF; Oikonomou A; Plataniotis KN; Mohammadi A; | 34113843 ENCS |
| 10 | COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images. | Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A | 32958971 ENCS |
| 11 | PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models. | Shahtalebi S; Atashzar SF; Samotus O; Patel RV; Jog MS; Mohammadi A; | 32042111 ENCS |
| Title: | DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis | ||||
| Authors: | Karimi R, Mohammadi A, Asif A, Benali H | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/35408182/ | ||||
| DOI: | 10.3390/s22072568 | ||||
| Publication: | Sensors (Basel, Switzerland) | ||||
| Keywords: | Brain Computer Interfaces; EEG signals; steady-state motion evoked potentials; | ||||
| PMID: | 35408182 | Category: | Date Added: | 2022-04-12 | |
| Dept Affiliation: |
ENCS
1 Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W. EV-009.187, Montreal, QC H3G 1M8, Canada. 2 Concordia Institute for Information System Engineering, Concordia University, 1455 De Maisonneuve Blvd. W. EV-009.187, Montreal, QC H3G 1M8, Canada. 3 Department of Electrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada. |
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Description: |
Recent advancements in Electroencephalographic (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications, ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion Visual Evoked Potentials (SSmVEP), where motion stimulation is used to address key issues associated with conventional light flashing/flickering. Such benefits, however, come with the price of being less accurate and having a lower Information Transfer Rate (ITR). From this perspective, this paper focuses on the design of a novel SSmVEP paradigm without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, called the Dual Frequency Aggregated Steady-State motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length. Compared to conventional SSmVEPs, the proposed DF-SSmVEP framework consists of two motion modes integrated and shown simultaneously each modulated by a specific target frequency. The paper also develops a specific unsupervised classification model, referred to as the Bifold Canonical Correlation Analysis (BCCA), based on two motion frequencies per target. The corresponding covariance coefficients are used as extra features improving the classification accuracy. The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. The proposed DF-SSmVEP outperforms its counterparts and achieved an average ITR of 30.7 ± 1.97 and an average accuracy of 92.5 ± 2.04, while the Radial Zoom and Rotation result in average ITRs of 18.35 ± 1 and 20.52 ± 2.5, and average accuracies of 68.12 ± 3.5 and 77.5 ± 3.5, respectively. |
