Brain.me: A Low-Cost Brain Computer Interface for AAL Applications

2014 ◽  
pp. 223-231
Author(s):  
Niccolò Mora ◽  
V. Bianchi ◽  
I. De Munari ◽  
P. Ciampolini
Keyword(s):  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface

A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface

2018 ◽  
Author(s):  
Shivanthan A.C. Yohanandan ◽  
Isabell Kiral-Kornek ◽  
Jianbin Tang ◽  
Benjamin S. Mshford ◽  
Umar Asif ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals Performance Assessment of a Custom, Portable, and Low-Cost Brain–Computer Interface Platform

2017 ◽  
Vol 64 (10) ◽  
pp. 2313-2320 ◽  
Author(s):  
Colin M. McCrimmon ◽  
Jonathan Lee Fu ◽  
Ming Wang ◽  
Lucas Silva Lopes ◽  
Po T. Wang ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface

Electroencephalographic spectral analysis from a wireless low-cost brain-computer interface for symptom capture of auditory verbal hallucinations in schizophrenia

2020 ◽  
Vol 220 ◽  
pp. 297-299
Author(s):  
Patricia Fernández-Sotos ◽  
Beatriz García-Martínez ◽  
Jorge J. Ricarte ◽  
José M. Latorre ◽  
Eva M. Sánchez-Morla ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Auditory Verbal Hallucinations

scholarly journals Fast DCT algorithms for EEG data compression in embedded systems

2015 ◽  
Vol 12 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Darius Birvinskas ◽  
Vacius Jusas ◽  
Ignas Martisius ◽  
Robertas Damasevicius
Keyword(s):  
Embedded Systems ◽  
Data Compression ◽  
High Speed ◽  
Communication Channel ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Eeg Signals ◽  
Eeg Data ◽  
Transform Coefficients

Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required.

scholarly journals The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment

Computers ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 68
Author(s):  
Patrick Schembri ◽  
Maruisz Pelc ◽  
Jixin Ma
Keyword(s):  
Brain Computer Interface ◽  
Low Cost ◽  
User Preference ◽  
Computer Interface ◽  
Oddball Paradigm ◽  
Active Listening ◽  
P300 Speller ◽  
Non Invasive ◽  
Auditory Distractions ◽  
Visual P300

This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications.

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