Richard Jones

Adjunct ProfessorRichard Jones

Link 306
Mobile: 021 063 7614

Research Interests

My research interests and contributions fall largely within neural engineering and the neurosciences, and particularly within (1) human performance engineering – development and application of computerized tests for quantification of upper-limb sensory-motor and cognitive function, particularly in brain disorders (stroke, Parkinson’s disease, traumatic brain injury) and driver assessment, (2) lapses of responsiveness (microsleeps, attention lapses) – characteristics, brain mechanisms via simultaneous-fMRI+EEG+Tracking+EyeClosure, and detection from behavioural measures (tracking and videometrics) and electrophysiological signals (EEG, EOG), (3) signal processing in clinical neurophysiology – detection of epileptic activity, (4) eye movements in brain disorders, (5) computational modelling of the human brain, (6) neural control of swallowing, (7) obstructive sleep apnea and adverse effects on brain, and (8) forensic brainwave analysis.

Recent Publications

  • Afzali U., Palmer R., Neumann E., Seren-Grace A., Makarios S., Wilson D. and Jones R. (2021) Detection of concealed knowledge via the ERP-based technique Brain Fingerprinting: Real-crime scenarios. Brisbane: Australasian Experimental Psychology Society, 7 Apr 2021.
  • Ayyagari S., Jones R. and Weddell S. (2021) Detection of microsleep states from the EEG: A comparison of feature reduction methods. Medical and Biological Engineering and Computing 59 MBEC-D-20-00240R2: 1643-1657.
  • Kornisch M., Robb MP. and Jones RD. (2020) Estimates of functional cerebral hemispheric differences in monolingual and bilingual people who stutter: dichotic listening paradigm. Clinical Linguistics and Phonetics 34(8): 774-789.
  • Guiu Hernandez E., Gozdzikowska K., Jones RD. and Huckabee ML. (2019) Pharyngeal Swallowing During Wake and Sleep. Dysphagia 34(6): 916-921.
  • Krishnamoorthy V., Shoorangiz R., Weddell S., Beckert L. and Jones R. (2019) Deep Learning with Convolutional Neural Network for detecting microsleep states from EEG: A comparison between the oversampling technique and cost-based learning. In IEEE Xplore.