Outputs (13)

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation (2019)
Conference Proceeding
Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. In 2019 International Conference on Robotics and Automation (ICRA) ; proceedings (4889-4895). https://doi.org/10.1109/icra.2019.8794060

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-... Read More about Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation.

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification (2019)
Conference Proceeding
Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification. In 2019 International Joint Conference on Neural Networks (IJCNN) ; proceedings (1-8). https://doi.org/10.1109/ijcnn.2019.8852227

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subj... Read More about Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification.

On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications (2019)
Journal Article
Podmore, J., Breckon, T., Aznan, N., & Connolly, J. (2019). On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 611-618. https://doi.org/10.1109/tnsre.2019.2904791

Brain-computer interfaces (BCI) harnessing Steady State Visual Evoked Potentials (SSVEP) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with alp... Read More about On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications.

On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks (2018)
Conference Proceeding
Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018): Miyazaki, Japan, 7-10 October 2018 (3726-3731). https://doi.org/10.1109/smc.2018.00631

Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG)... Read More about On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks.

Human neuroimaging reveals the subcomponents of grasping, reaching and pointing actions (2017)
Journal Article
Cavina-Pratesi, C., Connolly, J., Monaco, S., Figley, T., Milner, A., Schenk, T., & Culham, J. (2018). Human neuroimaging reveals the subcomponents of grasping, reaching and pointing actions. Cortex, 98, 128-148. https://doi.org/10.1016/j.cortex.2017.05.018

Although the neural underpinnings of visually guided grasping and reaching have been well delineated within lateral and medial fronto-parietal networks (respectively), the contributions of subcomponents of visuomotor actions have not been explored in... Read More about Human neuroimaging reveals the subcomponents of grasping, reaching and pointing actions.

Gender differences in non-standard mapping tasks: A kinematic study using pantomimed reach-to-grasp actions (2016)
Journal Article
Copley-Mills, F., Connolly, J., & Cavina-Pratesi, C. (2016). Gender differences in non-standard mapping tasks: A kinematic study using pantomimed reach-to-grasp actions. Cortex, 82, 244-254. https://doi.org/10.1016/j.cortex.2016.06.009

Comparison between real and pantomimed actions is used in neuroscience to dissociate stimulus-driven (real) as compared to internally driven (pantomimed) visuomotor transformations, with the goal of testing models of vision (Milner & Goodale, 1995) a... Read More about Gender differences in non-standard mapping tasks: A kinematic study using pantomimed reach-to-grasp actions.

Coding of attention across the human intraparietal sulcus (2015)
Journal Article
Connolly, J., Kentridge, R., & Cavina-Pratesi, C. (2016). Coding of attention across the human intraparietal sulcus. Experimental Brain Research, 234(3), 917-930. https://doi.org/10.1007/s00221-015-4507-2

There has been concentrated debate over four decades as to whether or not the nonhuman primate parietal cortex codes for intention or attention. In nonhuman primates, certain studies report results consistent with an intentional role, whereas others... Read More about Coding of attention across the human intraparietal sulcus.

Representational content of occipitotemporal and parietal tool areas (2015)
Journal Article
Bracci, S., Cavina-Pratesi, C., Connolly, J., & Ietswaart, M. (2016). Representational content of occipitotemporal and parietal tool areas. Neuropsychologia, 84, 81-88. https://doi.org/10.1016/j.neuropsychologia.2015.09.001

It is now established that the perception of tools engages a left-lateralized network of frontoparietal and occipitotemporal cortical regions. Nevertheless, the precise computational role played by these areas is not yet well understood. To address t... Read More about Representational content of occipitotemporal and parietal tool areas.

Non-obstructing 3D depth cues influence reach-to-grasp kinematics (2014)
Journal Article
Worssam, C. J., Meade, L. C., & Connolly, J. D. (2015). Non-obstructing 3D depth cues influence reach-to-grasp kinematics. Experimental Brain Research, 233(2), 385-396. https://doi.org/10.1007/s00221-014-4119-2

It has been demonstrated that both visual feedback and the presence of certain types of non-target objects in the workspace can affect kinematic measures and the trajectory path of the moving hand during reach-to-grasp movements. Yet no study to date... Read More about Non-obstructing 3D depth cues influence reach-to-grasp kinematics.

Gaze-dependent topography in human posterior parietal cortex (2013)
Journal Article
Connolly, J. D., Vuong, Q. C., & Thiele, A. (2015). Gaze-dependent topography in human posterior parietal cortex. Cerebral Cortex, 25(6), 1519-1526. https://doi.org/10.1093/cercor/bht344

The brain must convert retinal coordinates into those required for directing an effector. One prominent theory holds that, through a combination of visual and motor/proprioceptive information, head-/body-centered representations are computed within t... Read More about Gaze-dependent topography in human posterior parietal cortex.