N-Nieto

Feasibility_Analysis_Inner_Speech

Brain Computer Interfaces (BCIs) are useful devices that provide new ways of communication to people who have lost the capability of interacting with their environment. Although several paradigms have resulted in large improvements in the construction of BCIs, quite often they require great efforts from the patient or they are not able to generate natural and efficient interfaces. In that scenario, inner speech appears as a promising paradigm for tackling those problems. Nevertheless, the lack of publicly available databases largely precludes the analysis and development of methods for using this paradigm. In this work we use a recently released database to show that it is possible to classify and differentiate inner speech signals from signals acquired within other two well known paradigms. This is undoubtedly a first step in the search and construction of an inner speech based BCI.

GenderBias_CheXNet

Gender Bias Extra Material

Inner_Speech_Dataset

Codes to reproduce the Inner speech Dataset publicated by Nieto et al.

julearn_sk_pandas_nick

Forschungszentrum Jülich Machine Learning Library

MACRO_experiments

MODS_CULPRIT_project

N-Nieto

QC

QC first repo.

rBOTDA

Scrips for generate a robust Optimal Transport method

Relevance_Based_Pruning

Codes for Relevance-Based Pruning

otda-mibci

Python codes for applying OTDA in MI-BCI

N-Nieto/Inner_Speech_Dataset

Codes to reproduce the Inner speech Dataset publicated by Nieto et al.

N-Nieto/GenderBias_CheXNet

Gender Bias Extra Material

N-Nieto/Feasibility_Analysis_Inner_Speech

Brain Computer Interfaces (BCIs) are useful devices that provide new ways of communication to people who have lost the capability of interacting with their environment. Although several paradigms have resulted in large improvements in the construction of BCIs, quite often they require great efforts from the patient or they are not able to generate natural and efficient interfaces. In that scenario, inner speech appears as a promising paradigm for tackling those problems. Nevertheless, the lack of publicly available databases largely precludes the analysis and development of methods for using this paradigm. In this work we use a recently released database to show that it is possible to classify and differentiate inner speech signals from signals acquired within other two well known paradigms. This is undoubtedly a first step in the search and construction of an inner speech based BCI.

N-Nieto/MACRO_experiments

MODS_CULPRIT_project

N-Nieto/rBOTDA

Scrips for generate a robust Optimal Transport method

N-Nieto/Relevance_Based_Pruning

Codes for Relevance-Based Pruning

N-Nieto/julearn_sk_pandas_nick

Forschungszentrum Jülich Machine Learning Library

N-Nieto/N-Nieto

N-Nieto/QC

QC first repo.