A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments.

An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.

A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings.

Wearable electroencephalography (EEG) has the potential to improve everyday life through brain-computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed EEG systems that can still collect neural activity precisely. For example, researchers are using flexible EEG electrode arrays that can be attached around the ear (cEEGrids) to study neural activations in everyday life situations. However, the use of such concealed EEG approaches is limited by measurement challenges such as reduced signal amplitudes and high recording system costs. In this article, we compare the performance of a lower-cost open-source amplification system, the OpenBCI Cyton+Daisy boards, with a benchmark amplifier, the MBrainTrain Smarting Mobi. Our results show that the OpenBCI system is a viable alternative for concealed EEG research, with highly similar noise performance, but slightly lower timing precision. This system can be a great option for researchers with a smaller budget and can, therefore, contribute significantly to advancing concealed EEG research.

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299.

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks.

Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.

Telemetric electroencephalography recording in anesthetized mice-A novel system using minimally-invasive needle electrodes with a wireless OpenBCI™ Cyton Biosensing Board.

Telemetric electroencephalography (EEG) recording, using subdermal needle electrodes, is a minimally-invasive method to investigate mammalian neurophysiology during anesthesia. These inexpensive systems may streamline experiments examining global brain phenomena during surgical anesthesia or disease. We utilized the OpenBCI™ Cyton board with subdermal needle electrodes to extract EEG features in six C57BL/6J mice undergoing isoflurane anesthesia. Burst suppression ratio (BSR) and spectral features were compared for a verification of our method. Following an increase from 1.5% to 2.0% isoflurane, the BSR increased (Wilcoxon-signed-rank statistic; p = 0.0313). Furthermore, although the absolute EEG spectral power decreased, the relative spectral power remained comparable (Wilcoxon-Mann-Whitney U-Statistic; 95% CI exclusive AUC=0.5; p < 0.05). Compared to tethered systems, this method confers several improvements for anesthesia specific protocols: 1-Avoiding electrode implant surgical procedures, 2-Anatomical non-specificity for needle electrode placement to monitor global cortical activity representative of anesthetic state, 3-Facility to repeat recordings in the same animal, 4-User-friendly for non-experts, 5-Rapid set-up time, and 6-Lower costs.•Minimally-invasive telemetric EEG recording systems ergonomically improve tethered systems for anesthesia protocols.•Using this method, we verified that higher isoflurane concentrations resulted in an increased EEG burst suppression ratio and decreased EEG absolute spectral power, with no change in frequency distribution.

A simplified design of a cEEGrid ear-electrode adapter for the OpenBCI biosensing platform.

We present a simplified design of an ear-centered sensing system built around the OpenBCI Cyton & Daisy biosignal amplifiers and the flex-printed cEEGrid ear-EEG electrodes. This design reduces the number of components that need to be sourced, reduces mechanical artefacts on the recording data through better cable placement, and simplifies the assembly. Besides describing how to replicate and use the system, we highlight promising application scenarios, particularly the observation of large-amplitude activity patterns (e.g., facial muscle activities) and frequency-band neural activity (e.g., alpha and beta band power modulations for mental workload detection). Further, examples for common measurement artefacts and methods for removing them are provided, introducing a prototypical application of adaptive filters to this system. Lastly, as a promising use case, we present findings from a single-user study that highlights the system's capability of detecting jaw clenching events robustly when contrasted with 26 other facial activities. Thereby, the system could, for instance, be used to devise applications that reduce pathological jaw clenching and teeth grinding (bruxism). These findings underline that the system represents a valuable prototyping platform for advancing ear-based electrophysiological sensing systems and a low-cost alternative to current commercial alternatives.

Using Facial Micro-Expressions in Combination With EEG and Physiological Signals for Emotion Recognition.

Emotions are multimodal processes that play a crucial role in our everyday lives. Recognizing emotions is becoming more critical in a wide range of application domains such as healthcare, education, human-computer interaction, Virtual Reality, intelligent agents, entertainment, and more. Facial macro-expressions or intense facial expressions are the most common modalities in recognizing emotional states. However, since facial expressions can be voluntarily controlled, they may not accurately represent emotional states. Earlier studies have shown that facial micro-expressions are more reliable than facial macro-expressions for revealing emotions. They are subtle, involuntary movements responding to external stimuli that cannot be controlled. This paper proposes using facial micro-expressions combined with brain and physiological signals to more reliably detect underlying emotions. We describe our models for measuring arousal and valence levels from a combination of facial micro-expressions, Electroencephalography (EEG) signals, galvanic skin responses (GSR), and Photoplethysmography (PPG) signals. We then evaluate our model using the DEAP dataset and our own dataset based on a subject-independent approach. Lastly, we discuss our results, the limitations of our work, and how these limitations could be overcome. We also discuss future directions for using facial micro-expressions and physiological signals in emotion recognition.

The performance of a low-cost bio-amplifier on 3D human arm movement reconstruction.

Objectives In this study, the performance of OpenBCI, a low-cost bio-amplifier, is assessed when used for 3D motion reconstruction. Methods Eleven scalp electrode locations from three subjects were used, with sampling rate of 125 Hz, subsequently band-pass filtered from 0.5 to 40 Hz. After segmentation into epochs, information-rich frequency ranges were determined using filter bank common spatial filter. Simultaneously, the actual hand motions of subjects were captured using a Microsoft Kinect sensor. Multimodal data streams were synchronized using the lab streaming layer (LSL) application. A modified version of an existing multiple linear regression models was employed to learn the relationship between the electroencephalography (EEG) feature input and the recorded kinematic data. To assess system performance with limited data, 10-fold cross validation was used. Results The most information-rich frequency bands for subjects were found to be in the ranges of 5 - 9 Hz and 33 - 37 Hz. Hand lateralization accuracy for the three subjects were 97.4, 78.7 and 96.9% respectively. 3D position reconstructed with an average correlation coefficient of 0.21, 0.47 and 0.38 respectively along three pre-defined axes, with the corresponding average correlation coefficients for velocity being 0.21, 0.36 and 0.25 respectively. The results compare favourably with a cross-section of existing results, while cost-per-electrode costs were 76% lower than the average per-electrode cost for similar systems and 44% lower than the cheapest previously-reported system. Conclusions This study has shown that low-cost bio-amplifiers such as the OpenBCI can be used for 3D motion reconstruction tasks.

Assessment of changes in the electrical activity of the brain during general anesthesia using portable electroencephalography / Evaluación de los cambios de la actividad eléctrica cerebral durante anestesia general usando electroencefalografía portable

Abstract Introduction The analysis of the electrical activity of the brain using scalp electrodes with electroencephalography (EEG) could reveal the depth of anesthesia of a patient during surgery. However, conventional EEG equipment, due to its price and size, are not a practical option for the operating room and the commercial units used in surgery do not provide access to the electrical activity. The availability of low-cost portable technologies could provide for further research on the brain activity under general anesthesia and facilitate our quest for new markers of depth of anesthesia. Objective To assess the capabilities of a portable EEG technology to capture brain rhythms associated with the state of consciousness and the general anesthesia status of surgical patients anesthetized with propofol. Methods Observational, cross-sectional study that reviewed 10 EEG recordings captured using OpenBCI portable low-cost technology, in female patients undergoing general anesthesia with propofol. The signal from the frontal electrodes was analyzed with spectral analysis and the results were compared against the reports in the literature. Results The signal captured with frontal electrodes, particularly α rhythm, enabled the distinction between resting with eyes closed and with eyes opened in a conscious state, and sustained anesthesia during surgery. Conclusions It is possible to differentiate a resting state from sustained anesthesia, replicating previous findings with conventional technologies. These results pave the way to the use of portable technologies such as the OpenBCI tool, to explore the brain dynamics during anesthesia.

Resumen Introducción El análisis de la actividad eléctrica cerebral mediante electrodos ubicados sobre el cuero cabelludo con electroencefalografía (EEG) podría permitir conocer la profundidad anestésica de un paciente durante cirugía. Sin embargo, los equipos de EEG convencionales, por su precio y tamaño, no son una alternativa práctica en quirófanos y los equipos comerciales usados en cirugía no permiten acceder a la actividad eléctrica. Disponer de tecnologías portables y de bajo costo aumentaría el número de investigaciones sobre la actividad cerebral bajo anestesia general y facilitaría la búsqueda de nuevos marcadores para la profundidad anestésica. Objetivo Evaluar la capacidad de una tecnología EEG portable de adquirir ritmos cerebrales relacionados con el estado consciente y el estado de anestesia general de pacientes en cirugía anestesiados con propofol. Métodos Estudio observacional de corte transversal en el que se analizaron datos de 10 registros EEG obtenidos mediante tecnología portable y de bajo costo OpenBCI, de pacientes de sexo femenino que fueron sometidas a anestesia general con propofol. La señal obtenida de los electrodos frontales se analizó mediante análisis espectral y se contrastaron los resultados con lo descrito en la literatura. Resultados La señal obtenida con electrodos frontales, especialmente el ritmo α, permitió diferenciar el reposo con ojos cerrados y ojos abiertos en estado consciente, del estado de mantenimiento de la anestesia durante cirugía. Conclusiones Se logra la diferenciación de estado de reposo y de mantenimiento de la anestesia replicando hallazgos previos de tecnologías convencionales. Estos resultados abren la posibilidad de utilizar las tecnologías portables como el OpenBCI para investigar la dinámica cerebral durante la anestesia.