@article {34915,
	title = {Hybrid Soft-Rigid Active Prosthetics Laboratory Exercise for Hands-On Biomechanical and Biomedical Engineering Education},
	journal = {Journal of Biomechanical Engineering},
	year = {2024},
	url = {https://doi.org/10.1115/1.4065008},
	author = {Runze Gao and Peter Lee and Aravind Ravi and Carolyn Ren and Clark R Dickerson and James Tung}
}
@conference {31761,
	title = {Automated Sleep Staging on Wearable EEG Enables Sleep Analysis at Scale},
	booktitle = {11th International IEEE/EMBS Conference on Neural Engineering, NER},
	year = {2023},
	author = {Maurice Abou Jaoude and Aravind Ravi and Jiansheng Niu and Hubert Banville and Nicolas Florez Torres and Christopher Aimone}
}
@conference {31760,
	title = {Combined Action Observation, Motor Imagery and SSMVEP BCI Enhances Movement Related Cortical Potential},
	booktitle = {11th International IEEE/EMBS Conference on Neural Engineering, NER},
	year = {2023},
	author = {Aravind Ravi and James Tung and Ning Jiang}
}
@conference {31154,
	title = {Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations},
	booktitle = {arXiv},
	year = {2022},
	abstract = {Breast cancer is one of the leading causes of female mortality in the world. This can be reduced when diagnoses are performed at the early stages of progression. Further, the efficiency of the process can be significantly improved with computer aided diagnosis. Deep learning based approaches have been successfully applied to achieve this. One of the limiting factors for training deep networks in a supervised manner is the dependency on large amounts of expert annotated data. In reality, large amounts of unlabelled data and only small amounts of expert annotated data are available. In such scenarios, transfer learning approaches and self-supervised learning (SSL) based approaches can be leveraged. In this study, we propose a novel self-supervision pretext task to train a convolutional neural network (CNN) and extract domain specific features. This method was compared with deep features extracted using pre-trained CNNs such as DenseNet-121 and ResNet-50 trained on ImageNet. Additionally, two types of patch-combination methods were introduced and compared with majority voting. The methods were validated on the BACH microscopy images dataset. Results indicated that the best performance of 99\% sensitivity was achieved for the deep features extracted using ResNet50 with concatenation of patch-level embedding. Preliminary results of SSL to extract domain specific features indicated that with just 15\% of unlabelled data a high sensitivity of 94\% can be achieved for a four class classification of microscopy images.},
	url = {https://arxiv.org/abs/2202.06073},
	author = {Aravind Ravi}
}
@article {30216,
	title = {Enhanced System Robustness of Asynchronous BCI in Augmented Reality using Steady-state Motion Visual Evoked Potential},
	journal = {IEEE Transactions in Neural Systems and Rehabilitation Engineering},
	year = {2022},
	abstract = {This study evaluated the effect of change in background on steady state visually evoked potentials (SSVEP) and steady state motion visually evoked potentials (SSMVEP) based brain computer interfaces (BCI) in a small-profile augmented reality (AR) headset. A four target SSVEP and SSMVEP BCI was implemented using the Cognixion AR headset prototype. An active (AB) and a non-active background (NB) were evaluated. The signal characteristics and classification performance of the two BCI paradigms were studied. Offline analysis was performed using canonical correlation analysis (CCA) and complex-spectrum based convolutional neural network (C-CNN). Finally, the asynchronous pseudo-online performance of the SSMVEP BCI was evaluated. Signal analysis revealed that the SSMVEP stimulus was more robust to change in background compared to SSVEP stimulus in AR. The decoding performance revealed that the C-CNN method outperformed CCA for both stimulus types and NB background, in agreement with results in the literature. The average offline accuracies for W=1s of C-CNN were (NB vs. AB): SSVEP: 82\% {\textpm}15\% vs. 60\% {\textpm}21\% and SSMVEP: 71.4\% {\textpm} 22\% vs. 63.5\% {\textpm} 18\%. Additionally, for W=2s, the AR-SSMVEP BCI with the C-CNN method was 83.3\% {\textpm} 27\% (NB) and 74.1\% {\textpm}22\% (AB). The results suggest that with the C-CNN method, the AR-SSMVEP BCI is both robust to change in background conditions and provides high decoding accuracy compared to the AR-SSVEP BCI. This study presents novel results that highlight the robustness and practical application of SSMVEP BCIs developed with a low-cost AR headset.},
	url = {https://ieeexplore.ieee.org/document/9672118},
	author = {Aravind Ravi and Jing Lu and Sarah Pearce and Ning Jiang}
}
@conference {29415,
	title = {Asynchronous SSMVEP BCI and Influence of Dynamic Background in Augmented Reality},
	booktitle = {The 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP 2021)},
	year = {2021},
	abstract = {One of the important challenges in designing an SSMVEP-based Brain-computer interface (BCI) under any augmented reality (AR) environment is the stability of the system under dynamically changing background conditions. In this study, a novel AR optical see-through (OST) light-weight headset was incorporated into a BCI system based on steady state-motion-visually evoked potential (SSMVEP). The AR-BCI system was evaluated in a five-class asynchronous scenario, with four intentional control (IC) states and one no-control (NC) state. The system was tested under two background conditions: active (AB) and non-active (NB) background. Offline analysis was performed to evaluate the IC vs. NC classification performance using the complex-spectrum based convolutional neural network (C-CNN). The average performance metrics for 1-second window length were (NB vs. AB): Accuracy: 83\%{\textpm}9\% vs. 81\%{\textpm}8\%, F1-score: 0.82{\textpm}0.1 vs. 0.80{\textpm}0.09 and False Activation Rate: 16\%{\textpm}11\% vs. 17\%{\textpm}10\%. The results suggested that the C-CNN method applied on the SSMVEP responses in AR provides high IC vs. NC classification performance, and more importantly, is robust to change in dynamically changing background conditions.},
	url = {https://doi.org/10.1109/M2VIP49856.2021.9665078},
	author = {Aravind Ravi and Jing Lu and Sarah Pearce and Ning Jiang}
}
@conference {26883,
	title = {Does Inter-Stimulus Distance Influence the Decoding Performance of SSVEP and SSMVEP BCI?},
	booktitle = {2021 IEEE EMBS Neural Engineering (NER) Conference},
	year = {2021},
	abstract = {Brain-computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEP) are considered as one of the most successful noninvasive BCI paradigms. Recently, an evolution of SSVEP was introduced with repetitive motion as visual stimuli instead of the conventional flickering stimuli. This novel paradigm is steady-state motion visually evoked potential (SSMVEP), and it has been proposed to address some inherent limitations of the flickering stimulus. One important factor that affects the decoding performance of BCIs based on repetitive visual stimulus is the change in inter-stimulus distance (ISD) or competing stimuli. In this study, a preliminary analysis was performed to compare the influence of change in ISD on the decoding performance of SSVEP and SSMVEP paradigms. The experiments were performed on thirteen healthy participants in which two visual stimulus types: flickering stimuli and checkerboards stimuli, were presented. Three different ISDs were studied. The decoding performance was quantified by the canonical correlation analysis (CCA) on O1, O2, and Oz channels. With 1-second window length, the average decoding accuracy across all ISDs was 75\% for the SSVEP stimulus and 83.6\% for the SSMVEP stimulus, respectively, and the corresponding average variation across ISDs was 0.13 and 0.03. The results demonstrated striking robustness of SSMVEP against competing stimuli while maintaining an overall superior accuracy to that of the conventional SSVEP.},
	url = {https://ieeexplore.ieee.org/document/9441069},
	author = {Yuanpei Gao and Aravind Ravi and Ning Jiang}
}
@article {29414,
	title = {Effect of Competing Stimuli for Steady-state Visually Evoked Potential and Steady-state Motion Visually Evoked Potential},
	journal = {IEEE Access},
	year = {2021},
	abstract = {Changes in stimuli proximity have been shown to affect the performance of brain computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEP). Specifically, closely placed visual stimuli compete for neural representations, which is called the effect of competing stimuli. Recently, steady-state motion visual evoked potential (SSMVEP) has been proposed to alleviate some of the inherent limitations of SSVEP. In this study, the SSVEP and SSMVEP paradigms were systematically compared under three different inter-stimulus distances to modulate the effect of competing stimuli. Offline analysis was performed to study the steady-state response characteristics, strength of the responses and overall BCI decoding performance. Thirteen healthy subjects participated in the experiment; two types of visual stimulus with seven classes were presented: a flickering stimulus for SSVEP and a radial contraction-expansion checkerboard for the SSMVEP. The canonical correlation analysis (CCA) was used to study the signal characteristics and the offline decoding performance using only three EEG channels, O1, O2 and Oz. The results demonstrated that SSMVEP was not only less sensitive to competing stimuli, but also consistently outperformed SSVEP in their presence. Further, the SSMVEP response reached its steady-state faster than SSVEP. The signal characteristics analysis revealed that the SSVEP performances were better than SSMVEP in the lowest frequency tested (\&lt;9 Hz), and SSMVEP performance was significantly better in the highest frequencies investigated (\&gt;13 Hz). The findings in this study indicate that SSMVEP is likely a more practical BCI paradigm than the classic SSVEP for many real-world applications.},
	url = {https://ieeexplore.ieee.org/abstract/document/9536570},
	author = {Yuanpei Gao and Aravind Ravi and Ning Jiang}
}
@conference {21059,
	title = {Calibration-free SSVEP-based BCI Switch},
	booktitle = {INDICON 2019},
	year = {2020},
	url = {https://ieeexplore.ieee.org/abstract/document/9028973},
	author = {R.V. Sastry and S. Karthik and Adithya R. and Aravind Ravi and S. Indrapriyadarsini and Gagandeep Panwar and A.G. Ramakrishnan}
}
@article {24002,
	title = {Can a Highly Accurate Multi-class SSMVEP BCI Induce Sensory-motor Rhythm in Sensorimotor Area?},
	journal = {Journal of Neural Engineering},
	year = {2020},
	author = {Xin Zhang and Guanghua Xu and Aravind Ravi and Sarah Pearce and Ning Jiang}
}
@article {23554,
	title = {Comparing user-dependent and user-independent training of CNN for SSVEP BCI},
	journal = {Journal of Neural Engineering},
	year = {2020},
	abstract = {Objective. We presented a comparative study on the training methodologies of Convolutional Neural Network (CNN) for detection of steady-state visual evoked potentials (SSVEP). Two training scenarios were also compared: user-independent (UI) training and user-dependent (UD) training. Approach. The CNN was trained in both UD and UI scenarios on two types of features for SSVEP classification: magnitude spectrum features (M-CNN) and complex spectrum features (C-CNN). And the Canonical Correlation Analysis (CCA), widely used in SSVEP processing, was used as the baseline. Additional comparisons were performed with Task-Related Components Analysis (TRCA) and Filter-bank Canonical Correlation Analysis (FBCCA). The performance of the proposed CNN pipelines, CCA, FBCCA and TRCA were evaluated with two datasets: a seven-class SSVEP dataset collected on 21 healthy participants and a twelve-class publicly available SSVEP dataset collected on 10 healthy participants. Main results. The UD based training methods consistently outperformed the UI methods when all other conditions were the same, as one would expect. However, the proposed UI-C-CNN approach performed similar to the UD-M-CNN across all cases investigated on both datasets. On Dataset 1, the average accuracies of the different methods for 1 s window length were: CCA: 69.1{\textpm}10.8\%, TRCA: 13.4{\textpm}1.5\%, FBCCA: 64.8{\textpm}15.6\%, UI-M-CNN: 73.5{\textpm}16.1\%, UI-C-CNN: 81.6{\textpm}12.3\%, UD-M-CNN: 87.8{\textpm}7.6\% and UD-C-CNN: 92.5{\textpm}5\%. On Dataset 2, the average accuracies of the different methods for data length of 1 s were: UD-C-CNN: 92.33{\textpm}11.1\%, UD-M-CNN: 82.77{\textpm}16.7\%, UI-C-CNN: 81.6{\textpm}18\%, UI-M-CNN: 70.5{\textpm}22\%, FBCCA: 67.1{\textpm}21\%, CCA: 62.7{\textpm}21.5\%, TRCA: 40.4{\textpm}14\%. Using t-SNE, visualizing the features extracted by the CNN pipelines further revealed that the C-CNN method likely learned both the amplitude and phase related information from the SSVEP data for classification, resulting in superior performance than the M-CNN methods. The results suggested that UI-C-CNN method proposed in this study offers a good balance between performance and cost of training data. Significance. The proposed C-CNN based method is a suitable candidate for SSVEP-based BCIs and provides an improved performance in both UD and UI training scenarios.},
	url = {https://iopscience.iop.org/article/10.1088/1741-2552/ab6a67},
	author = {Aravind Ravi and Nargess Heydari Beni and Jacob Manuel and Ning Jiang}
}
@article {17735,
	title = {A Convolution Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential},
	journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
	year = {2019},
	abstract = {A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple states (sub-states) exist within the IC state. Existing recognition methods rely on a threshold technique, which is difficult to realize high accuracy, i.e., simultaneously high true positive rate and low false positive rate. To address this issue, we proposed a novel convolutional neural network (CNN) to detect IC and NC states in a SSVEP-BCI system for the first time. Specifically, the steady-state motion visual evoked potentials (SSMVEP) paradigm, which has been shown to induce less visual discomfort, was chosen as the experimental paradigm. Two processing pipelines were proposed for the detection of IC and NC states. The first one was using CNN as a multi-class classifier to discriminate between all the states in IC and NC state (FFT-CNN). The second one was using CNN to discriminate between IC and NC states, and using canonical correlation analysis (CCA) to perform classification tasks within the IC (FFT-CNN-CCA). We demonstrated that both pipelines achieved a significant increase in accuracy for low-performance healthy participants when traditional algorithms such as CCA threshold were used. Furthermore, the FFT-CNN-CCA pipeline achieved better performance than the FFT-CNN pipeline based on the stroke patients{\textquoteright} data. In summary, we showed that CNN can be used for robust detection in an asynchronous SSMVEP-BCI with great potential for out-of-lab BCI applications.},
	url = {https://ieeexplore.ieee.org/document/8708243},
	author = {Xin Zhang and Guanghua Xu and Xiang Mou and Aravind Ravi and Min Li and Yiwen Wang and Ning Jiang}
}
@conference {16690,
	title = {A Convolutional Neural Network for Enhancing the Detection of SSVEP in the Presence of Competing Stimuli},
	booktitle = {IEEE Engineering in Medicine and Biology Conference 2019},
	year = {2019},
	url = {https://ieeexplore.ieee.org/document/8857822},
	author = {Aravind Ravi and Jacob Manuel and Nargess Heydari and Ning Jiang}
}
@mastersthesis {19214,
	title = {Enhancing the Decoding Performance of Steady-State Visual Evoked Potentials based Brain-Computer Interface},
	year = {2019},
	type = {MASc. Thesis},
	abstract = {Non-invasive Brain-Computer Interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) responses are the most widely used BCI. SSVEP are responses elicited in the visual cortex when a user gazes at an object flickering at a certain frequency. In this thesis, we investigate different BCI system design parameters for enhancing the detection of SSVEP such as change in inter-stimulus distance (ISD), EEG channels, detection algorithms and training methodologies. Closely placed SSVEP stimuli compete for neural representations. This influences the performance and limits the flexibility of the stimulus interface. In this thesis, we study the influence of changing ISD on the decoding performance of an SSVEP BCI. We propose: (i) a user-specific channel selection method and (ii) using complex spectrum features as input to a convolutional neural network (C-CNN) to overcome this challenge.\&nbsp;We also evaluate the proposed C-CNN method in a user-independent (UI) training scenario as this will lead to a minimal calibration system and provide the ability to run inference in a plug-and-play mode. The proposed methods were evaluated on a 7-class SSVEP dataset collected on 21 healthy participants (Dataset 1). The UI method was also assessed on a publicly available 12-class dataset collected on 10 healthy participants (Dataset 2). We compared the proposed methods with canonical correlation analysis (CCA) and CNN classification using magnitude spectrum features (M-CNN). We demonstrated that the user-specific channel set (UC) is robust to change in ISD (viewing angles of 5.24ᵒ, 8.53ᵒ, and 12.23ᵒ) compared to the classic 3-channel set (3C - O1, O2, Oz) and 6-channel set (6C - PO3, PO4, POz, O1, O2, Oz). A significant improvement in accuracy of over 5\% (p=0.001) and a reduction in variation of 56\% (p=0.035) was achieved across ISDs using the UC set compared to the 3C set and 6C set. Secondly, the proposed C-CNN method obtained a significantly higher classification accuracy across ISDs and window lengths compared to M-CNN and CCA. The average accuracy of the C-CNN increased by over 12.8\% compared to CCA and an increase of over 6.5\% compared to the M-CNN for the closest ISD across all window lengths was achieved. Thirdly, the C-CNN method achieved the highest accuracy in both UD and UI training scenarios on both 7-class and 12-class SSVEP Datasets. The overall accuracies of the different methods for 1 s window length for Dataset 1 were: CCA: 69.1{\textpm}10.8\%, UI-M-CNN: 73.5{\textpm}16.1\%, UI-C-CNN: 81.6{\textpm}12.3\%, UD-M-CNN: 87.8{\textpm}7.6\% and UD-C-CNN: 92.5{\textpm}5\%. And for Dataset 2 were: CCA: 62.7{\textpm}21.5\%, UI-M-CNN: 70.5{\textpm}22\%, UI-C-CNN: 81.6{\textpm}18\%, UD-M-CNN: 82.8{\textpm}16.7\%, and UD-C-CNN: 92.3{\textpm}11.1\%. In summary, using the complex spectrum features, the C-CNN likely learned to use both frequency and phase related information to classify SSVEP responses. Therefore, the CNN can be trained independent of the ISD resulting in a model that generalizes to other ISDs. This suggests that the proposed methods are robust to changes in inter-stimulus distance for SSVEP detection and provides increased flexibility for user interface design of SSVEP BCIs for commercial applications. Finally, the UI method provides a virtually calibration free approach to SSVEP BCI.},
	url = {https://uwspace.uwaterloo.ca/handle/10012/14881},
	author = {Aravind Ravi}
}
@article {17961,
	title = {Fusing Frontal and Occipital EEG Features to Detect \&quot;Brain Switch\&quot; by Utilizing Convolutional Neural Network},
	journal = {IEEE Access},
	year = {2019},
	abstract = {Flicker is the most widely used steady-state visual evoked potential (SSVEP) stimulus. And checkerboard can induce steady-state motion visual evoked potential (SSMVEP) in the occipital area. More recently, the action video is proposed to simultaneously elicit SSMVEP and induce sensorimotor area activations via the mirror neuron systems through Action Observation (AO). Integration of AO with brain-computer interface (BCI) is appealing for neural rehabilitation applications. In order to make such a BCI paradigm more feasible in neural rehabilitation, it is essential to discriminate whether a user is actively engaged with the BCI, i.e. intentional control (IC) state, or not engaged, i.e. non-intentional control (NC) state. In this study, the EEG responses to these three types of visual stimuli were compared for the first time and a convolutional neural network (CNN) was proposed to discriminate IC and NC states. A visual gaiting stimulus was designed to realize BCI-based AO. The results showed that the power of alpha rhythms from frontal area decreased more when the participants engaged at the gaiting stimuli than when the participants engaged at the other two types of stimuli. And the correlation coefficient between the EEG from the occipital area and the template signals increased when the participants engaged at the stimuli. The results also clearly demonstrated the proposed CNN method can discriminate the IC and NC states. And the combination of the attention feature from the frontal area and SSVEP/SSMVEP feature from the occipital area showed significant performance improvement for the gaiting stimulus, but not for the other two types of stimuli.},
	url = {https://ieeexplore.ieee.org/document/8743389},
	author = {Xin Zhang and Guanghua Xu and Aravind Ravi and Wenqiang Yan and Ning Jiang}
}
@conference {18078,
	title = {A Hybrid BCI Approach to Detect Brain Switch in Action Observation by Utilizing Convolution Neural Network},
	booktitle = {IEEE Systems, Man and Cybernetics Conference 2019},
	year = {2019},
	url = {https://ieeexplore.ieee.org/abstract/document/8914363},
	author = {Xin Zhang and Guanghua Xu and Aravind Ravi and Sarah Pearce and Ning Jiang}
}
@conference {17962,
	title = {User-Independent SSVEP BCI Using Complex FFT Features and CNN Classification},
	booktitle = {IEEE Systems, Man and Cybernetics Conference 2019},
	year = {2019},
	url = {https://ieeexplore.ieee.org/document/8914258},
	author = {Aravind Ravi and Nargess Heydari and Ning Jiang}
}
@conference {15763,
	title = {User-Specific Channel Selection Method to Improve SSVEP BCI Decoding Robustness Against Variable Inter-Stimulus Distance},
	booktitle = {2019 IEEE EMBS Neural Engineering (NER) Conference},
	year = {2019},
	abstract = {Steady-state visual evoked potentials (SSVEP) are responses elicited when a user is presented with a repetitive visual stimulus. Change in stimuli proximity has been shown to have an influence on the performance of SSVEP-based BCI, where the inter-stimulus distance has a positive correlation with the overall performance. This limits the flexibility in stimulus design by imposing a constraint on the acceptable inter-stimulus distance, consequently limiting the range of applicability for SSVEP-based BCIs in real-world applications. Another limitation that needs to be addressed is the required number of EEG channels. In this study, we investigated these two challenges. A process of selecting optimal user-specific channel set was proposed. We demonstrated that the user-specific channel set is more robust against variable inter-stimulus distance. A significant improvement in accuracy (p=10\&nbsp;<sup>-3</sup>\&nbsp;) of 5\% and a reduction in variation (p=10\&nbsp;<sup>-3</sup>\&nbsp;) of 55\% was achieved on average when compared to the performance using the classic 3-channel set (O1, O2, Oz) and 6-channel set (O1, O2, Oz, PO3, PO4, POz).},
	url = {https://ieeexplore.ieee.org/abstract/document/8716912},
	author = {Aravind Ravi and Sarah Pearce and Xin Zhang and Ning Jiang}
}
@conference {15762,
	title = {A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features},
	booktitle = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)},
	year = {2018},
	address = { 2018 IEEE Symposium Series on Computational Intelligence (SSCI)},
	url = {https://ieeexplore.ieee.org/abstract/document/8628877},
	author = {Aravind Ravi and Harshwin Venugopal and Sruthy Paul and H.R. Tizhoosh}
}
@conference {15761,
	title = {Automatic User Customized Brain Switch},
	booktitle = { 2016 IEEE Annual India Conference (INDICON)},
	year = {2016},
	address = {INDICON 2016},
	url = {https://ieeexplore.ieee.org/abstract/document/7839045},
	author = {R.V. Sastry and Aravind Ravi and Gagandeep Panwar and S. Indrapriyadarsini and A.G. Ramakrishnan}
}
