activity
20182021
most citedRevisiting the Application of Feature Selection Methods to Speech Imagery BCI Datasets

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.HC2021

EmoconLite: Bridging the Gap Between Emotiv and Play for Children With Severe Disabilities

Javad Rahimipour Anaraki, Chelsea Anne Rauh, Jason Leung +1

Brain-computer interfaces (BCIs) allow users to control computer applications by modulating their brain activity. Since BCIs rely solely on brain activity, they have enormous poten…

cs.CV2020

A Deep Learning Approach to Tongue Detection for Pediatric Population

Javad Rahimipour Anaraki, Silvia Orlandi, Tom Chau

Children with severe disabilities and complex communication needs face limitations in the usage of access technology (AT) devices. Conventional ATs (e.g., mechanical switches) can…

q-bio.NC2020

A comparison of oscillatory characteristics in covert speech and speech perception

Jae Moon, Silvia Orlandi, Tom Chau

Covert speech, the silent production of words in the mind, has been studied increasingly to understand and decode thoughts. This task has often been compared to speech perception a…

cs.LG20202 cited

Revisiting the Application of Feature Selection Methods to Speech Imagery BCI Datasets

Javad Rahimipour Anaraki, Jae Moon, Tom Chau

Brain-computer interface (BCI) aims to establish and improve human and computer interactions. There has been an increasing interest in designing new hardware devices to facilitate…

cs.HC2018

Online classification of imagined speech using functional near-infrared spectroscopy signals

Alborz Rezazadeh Sereshkeh, Rozhin Yousefi, Andrew T Wong +1

Most brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS) require that users perform mental tasks such as motor imagery, mental arithmetic, or mu…