The RERC on AAC partnered with a student engineering Learning Factory team at Penn State University in the spring of 2019
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Effects of literacy instruction on single-word reading (Mandak et al., 2018)
Mandak et al. completed a systematic review to investigate the effects of instruction on single-word reading for individuals who use AAC.
The employment experiences of adults with ASD who use AAC (Richardson, McCoy, & McNaughton, 2019)
Richardson et al describe the employment experiences of 7 individuals with ASD who use AAC in the workplace.
Providing services in the inpatient rehabilitation setting (Gormley & Light, 2019)
Gormley and Light describe the experiences of 11 SLPs who work with people with complex communication needs within the inpatient rehabilitation setting
RERC on AAC: Progress on Research, Development, and Training Activities (McNaughton et al., 2019)
At the 2019 ATIA Conference, McNaughton et al. shared progress updates on the many research, development, and training activities of the RERC on AAC.
Cognitive Demands Checklist for AAC (Fried-Oken, et al., 2019)
At the 2019 ATIA Conference, Fried-Oken and colleagues discussed the Cognitive Demands Checklist for AAC—a tool to help identify the attention, memory, and executive function demands of different AAC technologies.
Training SLPs to provide adapted literacy instruction (Caron et al., 2019)
At the 2019 ATIA Conference, Caron and colleagues shared findings from a study in which SLPs were trained in adapted literacy instruction from online modules.
Effects of simulated visual acuity and ocular motility impairments on SSVEP BCI performance (Peters et al., 2018)
Peters et al. report on use of the Shuffle Speller typing interface for SSVEP BCI copy-spelling with simulated VAI, simulated OMI, and unimpaired vision.
Adversarial deep learning in EEG biometrics (Özdenizci et al., 2019)
Özdenizci et al. propose an adversarial inference approach to extend deep learning models to learn session-invariant person-discriminative representations.
Code-VEP vs. eye tracking: A comparison study (Nezamfar et al., 2018)
Nezamfar et al. compare the efficacy of a BCI input signal, code-VEP via Electroencephalography, against eye gaze tracking.