My research field is related to Neurofeedback, Signal Processing, Brain-Computer Interfaces and Human-Computer Interaction.

Current Work: Neurofeedback Sessions Measurement based on the user's Individual Alpha Peak Frequency

Goal: Present the Individual Alpha Peak Frequency (IAPF) as a standard for predicting the number of sessions of Neurofeedback the patient needs in order to have an improvement in its cognitive ability.

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

Written with: Gibran Etcheverry

Book: Converging Clinical and Engineering Research on Neurorehabilitation III

Available here.


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.

Newborn cry nonlinear features extraction and classification

Written with: Omar López-Rincón, David Rojas-Velazquez, Luis Oswaldo Valencia-Rosado, Roberto Rosas-Romero and Gibran Etcheverry

Journal: Journal of Intelligent and Fuzzy Systems

Available here.


Newborn cry features extraction for affections detection and classification has been intensively developed during the last ten to fifteen years. In this work, methods from the system identification area have been implemented in order to obtain ten Linear Predictive Coefficients (LPCs) plus a nonlinear one stated as Bilinear Intermittent Factor (BIF) per 20 ms analysis window for 40 normal and loss hearing (deaf) newborn cries each. In order to show the contribution of the nonlinear feature, a Kernel Discriminant Analysis (KDA) is performed and afterwards, two classifications tests employing Supported Vector machines (SVMs) as a standard and the Expectation Maximization (EM) algorithm over a Mixture of Experts (ME) operation, considering the BIF as an expert or parent of the LPCs, allows to obtain a 99.84% classification.

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

Available here.


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.

Bachelor's thesis

This thesis presents Malvasia, a BCI system where the users can interact with the objects displayed in a museum.

Thesis available here.

User experience design for brain-computer interfaces to support interaction in points of interest.

Written with: J. Alfredo Sánchez, Ofelia Cervantes.

Conference: Fifth Mexican Conference on Human-Computer Interaction (MEXIHC 2014)

Journal: Research in Computing Science

Available here.


This paper discusses the potential of brain-computer interfaces (BCI) in the interaction between users and objects in points of interest in a city. We present an initial design of the user experience with BCI, aimed to include users with disabilities but also to enhance the experience of the general public. This design includes a physical space to be conditioned specifically for BCI so users can interact with certain objects in a museum, as well as enhancements throughout the museum based on BCI. We report results of a formative evaluation of the main design concepts.