I am working as Australian Research Council Postdoctoral Research Fellow (PDF) at Australian Artificial Intelligence Institute in University of Technology Sydney (UTS). I have completed Ph.D. degree in Computer Science in May 2019 from UTS, Australia. Before my doctorate, I received the M.S. degree in Software Systems in 2013 from Birla Institute of Technology and Science Pilani, India, and B.Sc. degree in mathematics in 2007 from Chhatrapati Shahu Ji Maharaj University, India. During these years of study and work, I have worked on vast variety of topics in the field of human-computer interface (HCI), interdisciplinary neuroscience, and machin learning. I have developed several systems such as ‘Web-browser for blinds based on natural language processing,’ ‘A brain-computer interface (BCI) system for communication,' 'a brain-computer interface based eating-assistant system for the user with a disability,’ and ‘A closed-loop brain-computer interface system to evaluate and adapt user’s feeling in Virtual Reality,’ etc.
The current interests of my research are to integrate the artificial intelligence technologies with cognitive neuroscience knowledge for exploring the cognitive functions, discovering the relationships between brain dynamics, evaluated everyday interaction, decision making, and develop robust brain-computer interface.
Can supervise: YES
- Neural Engineering,
- Brain Computer Interface,
- Cognitive Neuroscience,
- Deep Learning
- Virtual and Augmented Reality
Singh, AK, Chen, H-T, Gramann, K & Lin, C-T 2020, 'Intraindividual Completion Time Modulates the Prediction Error Negativity in a Virtual 3-D Object Selection Task', IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, vol. 12, no. 2, pp. 354-360.View/Download from: Publisher's site
Singh, AK, Wang, Y-K, King, J-T & Lin, C-T 2020, 'Extended Interaction with a BCI Video Game Changes Resting-State Brain Activity', IEEE Transactions on Cognitive and Developmental Systems, pp. 1-1.View/Download from: Publisher's site
Pan, Y, Tsang, IW, Singh, AK, Lin, C-T & Sugiyama, M 2020, 'Stochastic Multichannel Ranking with Brain Dynamics Preferences', NEURAL COMPUTATION, vol. 32, no. 8, pp. 1499-1530.View/Download from: Publisher's site
Lin, CT, Chiu, C-Y, Singh, AK, King, J-T & Wang, YK 2019, 'A Wireless Multifunctional SSVEP-Based Brain Computer Interface Assistive System', IEEE Transactions on Cognitive and Developmental Systems, vol. 11, no. 3.View/Download from: Publisher's site
IEEE Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their non-portability, impracticality and inconvenience because of the adoption of wired or invasive electroencephalography (EEG) acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this study, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential (SSVEP)-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: video entertainment, video calling, and active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate (ITR) achieved is 90.91% and 24.94 bit per min respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.
Lin, CT, King, JT, Singh, AK, Prasad, M, Ma, Z, Lin, JW, Machado, AMC, Appaji, A & Gupta, A 2018, 'Voice Navigation Effects on Real-World Lane Change Driving Analysis using an Electroencephalogram', IEEE Access, vol. 6.View/Download from: Publisher's site
OAPA Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving. In this study, a real-world experiment is designed to acquire the electroencephalography (EEG) and in-car information to analyze the dynamic brain activity while the driver is performing the lane-changing task based on the auditory instructions from an in-car navigation system. The results show that auditory cues can influence the speed and increase the frontal EEG delta and beta power which is related to motor preparation and decision making during a lane change. However, there were no significant results on the alpha power. A better lane-change assessment can be obtained using specific vehicle information (lateral acceleration and heading angle) with EEG features for future naturalized driving study.
Singh, AK, Chen, HT, Cheng, YF, King, JT, Ko, LW, Gramann, K & Lin, CT 2018, 'Visual Appearance Modulates Prediction Error in Virtual Reality', IEEE Access, vol. 6, pp. 24617-24624.View/Download from: Publisher's site
© 2013 IEEE. Different rendering styles induce different levels of agency and user behaviors in virtual reality environments. We applied an electroencephalogram-based approach to investigate how the rendering style of the users' hands affects behavioral and cognitive responses. To this end, we introduced prediction errors due to cognitive conflicts during a 3-D object selection task by manipulating the selection distance of the target object. The results showed that, for participants with high behavioral inhibition scores, the amplitude of the negative event-related potential at approximately 50-250 ms correlated with the realism of the virtual hands. Concurring with the uncanny valley theory, these findings suggest that the more realistic the representation of the user's hand is, the more sensitive the user becomes toward subtle errors, such as tracking inaccuracies.
Singh, AK 2018, 'Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering', Journal of Healthcare Engineering, vol. 2018.View/Download from: Publisher's site
Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.
Lin, CT, Chuang, CH, Cao, Z, Singh, AK, Hung, CS, Yu, YH, Nascimben, M, Liu, YT, King, JT, Su, TP & Wang, SJ 2017, 'Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment', IEEE Access, vol. 5, pp. 10612-10621.View/Download from: Publisher's site
© 2013 IEEE. There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions.
Verma, P, Singh, R & Singh, AK 2013, 'A framework to integrate speech based interface for blind web users on the websites of public interest', Human-centric Computing and Information Sciences, vol. 3, no. 1, pp. 1-18.View/Download from: Publisher's site
Verma, P, Singh, R & Kumar Singh, A 2012, 'A Framework for the Next Generation Screen Readers for Visually Impaired', International Journal of Computer Applications, vol. 47, no. 10, pp. 31-38.View/Download from: Publisher's site
Despite shortcomings, Screen Readers have been the primary
tool for using internet by visually impaired. In this paper, we
present a framework for an advanced Screen Reader that aims
at eliminating the drawbacks that are associated with the
existing systems. The proposed framework makes the use of
informed search technique to enhance the usability and
navigability. Some of its features like background music to
appraise the layout structure of web page, mouse hovering to
speak out glimpses of the underlying text make the use of
image processing techniques. These features are implemented
independent of the rest development therefore they can also
be used to enhance any existing Screen Reader.
Singh, AK & Lin, C-T, 'EnK: Encoding time-information in convolution'.
Recent development in deep learning techniques has attracted attention in
decoding and classification in EEG signals. Despite several efforts utilizing
different features of EEG signals, a significant research challenge is to use
time-dependent features in combination with local and global features. There
have been several efforts to remodel the deep learning convolution neural
networks (CNNs) to capture time-dependency information by incorporating
hand-crafted features, slicing the input data in a smaller time-windows, and
recurrent convolution. However, these approaches partially solve the problem,
but simultaneously hinder the CNN's capability to learn from unknown
information that might be present in the data. To solve this, we have proposed
a novel time encoding kernel (EnK) approach, which introduces the increasing
time information during convolution operation in CNN. The encoded information
by EnK lets CNN learn time-dependent features in-addition to local and global
features. We performed extensive experiments on several EEG datasets: cognitive
conflict (CC), physical-human robot collaboration (pHRC), P300 visual-evoked
potentials, movement-related cortical potentials (MRCP). EnK outperforms the
state-of-art by 12\% (F1 score). Moreover, the EnK approach required only one
additional parameter to learn and can be applied to a virtually any CNN
architectures with minimal efforts. These results support our methodology and
show high potential to improve CNN performance in the context of time-series
data in general.
AbstractDetecting and correcting incorrect body movements is an essential part of everyday interaction with one's environment. The human brain has a constant monitoring system that controls and adjusts our actions according to our surroundings. However, when our brain's predictions about a planned action do not match the sensory inputs resulting from that action, cognitive conflict occurs. Much is known about cognitive conflict in 1D/2D environments; however, less is known about the role of movement characteristics on cognitive conflict in 3D environment. Hence, we devised an object selection task in a virtual reality environment to test how the velocity of hand movements impact a number of brain responses. From a series of analyses of EEG recordings synchronized with motion capture, we found that the velocity of the participants' hand movements modulated the brain's proprioception during the task and induced prediction error negativity. Additionally, prediction error negativity originates in the anterior cingulate cortex and is itself modulated by the ballistic phase of the hand's movement. These findings suggest that velocity is an essential component of integrating hand movements with visual and proprioceptive information during interactions with real and virtual objects.
Ahamed, S, Madan, P & Singh, AK 2019, 'Transhumanism in India: Past, Present and the Future' in Lee, N (ed), The Transhumanism Handbook, Springer, Switzerland, pp. 701-714.View/Download from: Publisher's site
Transhumanism is the philosophy or theory that hypothesizes that the human species can evolve beyond its present limited physical and mental capacities, especially with the help of science and technology. The Indian subcontinent has a particularly rich cultural heritage, which has a certain level of natural compatibility with the transhumanist core philosophies. From the pursuit of longevity to the morphological freedom exercised by the avatars of ancient polytheistic Gods and Goddesses, the legacy and heritage of ancient Indian civilizations have much more similitude with the radical concepts of transhumanism of the modern ages. Statistics show a significant possibility for India to emerge as one of the revered players in the field of economy, science, and technology by 2050. Such a promising scenario paves a way for ideas like transhumanism to play an important role in shaping the country's future. In addition, a wider acceptance of transhumanist concepts can be easily achieved in India by connecting the common Indian man to his roots, using layman's words instead of strictly academic terminologies.
Singh, AK, Aldini, S, Leong, D, Wang, YK, Carmichael, MG, Liu, D & Lin, CT 2020, 'Prediction Error Negativity in Physical Human-Robot Collaboration', 8th International Winter Conference on Brain-Computer Interface, BCI 2020.View/Download from: Publisher's site
© 2020 IEEE. Cognitive conflict is a fundamental phenomenon of human cognition, particularly during interaction with the real world. Understanding and detecting cognitive conflict can help to improve interactions in a variety of applications, such as in human-robot collaboration (HRC), which involves continuously guiding the semi-autonomous robot to perform a task in given settings. There have been several works to detect cognitive conflict in HRC but without physical control settings. In this work, we have conducted the first study to explore cognitive conflict using prediction error negativity (PEN) in physical human-robot collaboration (pHRC). Our results show that there was a statistically significant (p =. 047) higher PEN for conflict condition compared to normal conditions, as well as a statistically significant difference between different levels of PEN (p =. 020). These results indicate that cognitive conflict can be detected in pHRC settings and, consequently, provide a window of opportunities to improve the interaction in pHRC.
Aldini, S, Akella, A, Singh, A, Wang, Y-K, Carmichael, M, Liu, D & Lin, C-T 2019, 'Effect of Mechanical Resistance on Cognitive Conﬂict in Physical Human-Robot Collaboration', https://ieeexplore.ieee.org/xpl/conhome/8780387/proceeding, International Conference on Robotics and Automation, IEEE, Canada, pp. 6137-6143.View/Download from: Publisher's site
Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (p <; 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive.
Gehrke, L, Akman, S, Lopes, P, Chen, A, Singh, AK, Chen, H-T, Lin, CT & Gramann, K 2019, 'Detecting Visuo-Haptic Mismatches in Virtual Reality using the Prediction Error Negativity of Event-Related Brain Potentials', CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI Conference on Human Factors in Computing Systems (CHI), ASSOC COMPUTING MACHINERY, Glasgow, SCOTLAND.View/Download from: Publisher's site
Do, T, Lin, C, Cortes, C, Singh, A, Liu, J, Chen, H & Gramann, K 2019, 'Human brain dynamics during navigation with natural walking under different workload conditions in virtual reality by using the mobile brain/body imaging approach', Society for Neuroscience, Chicago.
Singh, A 2019, 'Investigating the impact of landmarks on spatial learning during active navigation', Society for Neuroscience, Chicago.
Chiu, CY, Singh, AK, Wang, YK, King, JT & Lin, CT 2017, 'A wireless steady state visually evoked potential-based BCI eating assistive system', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Anchorage, AK, USA, pp. 3003-3007.View/Download from: Publisher's site
© 2017 IEEE. Brain-Computer interface (BCI) which aims at enabling users to perform tasks through their brain waves has been a feasible and worth developing solution for growing demand of healthcare. Current proposed BCI systems are often with lower applicability and do not provide much help for reducing burdens of users because of the time-consuming preparation required by adopted wet sensors and the shortage of provided interactive functions. Here, by integrating a state visually evoked potential (SSVEP)-based BCI system and a robotic eating assistive system, we propose a non-invasive wireless steady state visually evoked potential (SSVEP)-based BCI eating assistive system that enables users with physical disabilities to have meals independently. The analysis compared different methods of classification and indicated the best method. The applicability of the integrated eating assistive system was tested by an Amyotrophic Lateral Sclerosis (ALS) patient, and a questionnaire reply and some suggestion are provided. Fifteen healthy subjects engaged the experiment, and an average accuracy of 91.35%, and information transfer rate (ITR) of 20.69 bit per min are achieved. For online performance evaluation, the ALS patient gave basic affirmation and provided suggestions for further improvement. In summary, we proposed a usable SSVEP-based BCI system enabling users to have meals independently. With additional adjustment of movement design of the robotic arm and classification algorithm, the system may offer users with physical disabilities a new way to take care of themselves.
Nascimben, M, Wang, YK, Singh, AK, King, JT & Lin, CT 2017, 'Influence of EEG tonic changes on Motor Imagery performance', 2017 8TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 8th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE, Shanghai, PEOPLES R CHINA, pp. 46-49.
Nascimben, M, Yu, Y-H, Lin, C-T, King, J-T, Singh, AK & Chuang, C 2016, 'Effect of a cognitive involving videogame on MI task', Proceedings of the 6th International Brain-Computer Interface Meeting, organized by the BCI Society, Verlag der TU Graz, Graz University of Technology, sponsored by g.tec medical engineering GmbH, pp. 175-175.View/Download from: Publisher's site
Researcher and developers have to face with performance variation in motor imagery across and
within subjects and its fluctuations over time. In addition MI achievement variations within subjects are closely
correlated to neurophysiological variables. In our study a MI task was submitted to a group of healthy subjects
before and after playing BCIGEM videogame for 90 minutes. Some EEG features were found, suggesting a
different pathway of activation inside MU rhythm during Motor Imagery (MI) after a mentally challenging activity
like playing a videogame
Singh, A, Wang, Y-K, Chiu, C-Y, Yu, Y-H, Nascimben, M, King, J-T, Chuang, C-H, Chen, S-A, Ko, L-W, Pal, NR & others 2016, 'Attention in Complex Environment of Brain Computer Interface', Proceedings of the 6th International Brain-Computer Interface Meeting, organized by the BCI Society, Verlag der TU Graz, Graz University of Technology, sponsored by g.tec medical engineering GmbH, pp. 165-165.
Singh, AK, Wang, Y-K, King, J-T, Lin, C-T & Ko, L-W 2015, 'A simple communication system based on Brain Computer Interface', Proceedings of the Technologies and Applications of Artificial Intelligence (TAAI), Technologies and Applications of Artificial Intelligence, IEEE, Tainan, Taiwan, pp. 363-366.View/Download from: Publisher's site
The current study presents one Brain Computer
Interface (BCI) based communication system in which the
intentional eye blink were extracted from the single channel EEG
data. This system could be useful for the sufferers of motor
disease, locked in syndrome, and paralysis. In particular, one new
Human Computer Interface (HCI) can also benefit healthy users.
To detect the intentional eye blinking in real-time, the score was
calculated from delta, theta and gamma power band from the
brain dynamics acquired from single channel through NeuroSky
Headset wirelessly. The soft and hard blink represent ' 0 ' and '1'
in this system respectively and formed four continuous bit string
which map to the pre-defined text in this system. The mapped
text was convert into speech and send to the speaker. The
experimental results shows that this system can provide an
accurate and convenient way to communicate through the brain
Singh, R, Verma, P & Singh, AK 2010, 'TC-GXML-A Transcoder for HTML to XML Grammar', Data Storage and Data Engineering (DSDE), 2010 International Conference on, IEEE, pp. 34-38.
Verma, P, Singh, R, Singh, AK, Yadav, V & Pandey, A 2010, 'An enhanced speech-based internet browsing system for visually challenged', Computer and Communication Technology (ICCCT), 2010 International Conference on, IEEE, pp. 724-730.
As virtual reality (VR) emerges as a mainstream platform, designers have
started to experiment new interaction techniques to enhance the user
experience. This is a challenging task because designers not only strive to
provide designs with good performance but also carefully ensure not to disrupt
users' immersive experience. There is a dire need for a new evaluation tool
that extends beyond traditional quantitative measurements to assist designers
in the design process. We propose an EEG-based experiment framework that
evaluates interaction techniques in VR by measuring intentionally elicited
cognitive conflict. Through the analysis of the feedback-related negativity
(FRN) as well as other quantitative measurements, this framework allows
designers to evaluate the effect of the variables of interest. We studied the
framework by applying it to the fundamental task of 3D object selection using
direct 3D input, i.e. tracked hand in VR. The cognitive conflict is
intentionally elicited by manipulating the selection radius of the target
object. Our first behavior experiment validated the framework in line with the
findings of conflict-induced behavior adjustments like those reported in other
classical psychology experiment paradigms. Our second EEG-based experiment
examines the effect of the appearance of virtual hands. We found that the
amplitude of FRN correlates with the level of realism of the virtual hands,
which concurs with the Uncanny Valley theory.
- University of New South Wales, Australia
- University of Newcastle, Australia
- Technical University of Berlin, Germany
- National Chiao Tung University, Taiwan
- University of San Diego Diego, USA