CONFERENCES

Use of Canonical Correlation Analysis for the study of adaptation capacity

Written with: Adam Turnbull, Mia Anthony, Ehsan Adeli, Feng Vankee Lin.

Conference: Neuroscience 2022

Available here.

Individual Alpha Peak Frequency’s Dataset Through Neurofeedback’s Protocol

Written with: Gibran Etcheverry

Conference: 4th International Conference on NeuroRehabilitation (ICNR 2018).

Available here.

ABSTRACT

The Individual Alpha Peak Frequency (IAF) is the individual dominant electroencephalogram (EEG) frequency in the range of n to m (n = 8 and m = 12). IAF is related to various cognitive functions such as attention and working memory; and can be affected by biological, psychological and social aspects. In this paper, a Neurofeedback (NF) protocol is presented, which takes into consideration these three aspects. The main purpose is to create an Individual Alpha Peak Frequency (IAPF) dataset for a NF system in order to predict the number of NF sessions for a cognitive skills improvement. Two studies were performed using this protocol with 10 students divided in experimental and control groups, where an advance in the IAPF (Frequency and Absolute Power) can be observed in the first group.

Neurofeedback sessions measurement based on the user’s Peak Alpha Frequency

Written with: Gibran Etcheverry

Poster presentation.

Conference: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17).

Available here.

A platform for experimenting with brain-computer interfaces in points of interest of smart cities

Written with: J. Alfredo Sánchez, Ofelia Cervantes and Wanggen Wan  

Conference: 7th Latin American Conference on Human-computer Interaction (CLIHC 2015).

Available here.

ABSTRACT

This paper presents a platform to support experimentation with applications that enhance user experience in points of interest (POIs) of smart cities by incorporating brain-computer interfaces (BCI). We propose a general architecture for applications in this realm that includes four major layers: Presentation, sensing, action management, and data management. This architecture can be instantiated with various types of BCI sensors and diverse POIs. We describe its components as well as a prototype based on this architecture. We also report on initial findings of the use of our prototype, which show the potential of our approach to support research in the area.