Single channel brain-computer interface control system based on TGAM module

Zhang, Lu; Lv, Qingsong; Xu, Yishen

doi:10.1109/cisp-bmei.2017.8302235

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…First of all, it may be the problem of development cost that TGAM, as a single-node EEG device, does not have good enough antinoise and data analysis step. No matter, in the experiment of this paper or the experimental data in some articles [21], the attention data calculated by the module's own algorithm is very unsatisfactory in practical application, with large fluctuations, high delay, and low accuracy (Figure 1). Some research projects have used large [22], but the results have been extremely limited.…”

Section: The Design Of Data Transmission Via the Tgammentioning

confidence: 87%

Attention Optimization Method for EEG via the TGAM

Xie

2020

Computational and Mathematical Methods in Medicine

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Since the 21st century, noninvasive brain-computer interface (BCI) has developed rapidly, and brain-computer devices have gradually moved from the laboratory to the mass market. Among them, the TGAM (ThinkGear Asic Module) and its encapsulate algorithm have been adopted by many research teams and faculty members around the world. However, due to the limited development cost, the effectiveness of the algorithm to calculate data is not satisfactory. This paper proposes an attention optimization algorithm based on the TGAM for EEG data feedback. Considering that the data output of the TGAM encapsulate algorithm fluctuates greatly, the delay is high and the accuracy is low. The experimental results demonstrated that our algorithm can optimize EEG data, so that with the same or even lower delay and without changing the encapsulate algorithm of the module itself, it can significantly improve the performance of attention data, greatly improve the stability and accuracy of data, and achieve better results in practical applications.

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Section: The Design Of Data Transmission Via the Tgammentioning

confidence: 87%

Attention Optimization Method for EEG via the TGAM

Xie

2020

Computational and Mathematical Methods in Medicine

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“…In addition, Physical Reconfiguration is made using a TGAM1 sensor for acquisition of dolphin brain signals and a Bluetooth HC06 B26782h module for wireless data transmission. This sensor has different connections, but among the most important are the power, transmission, reception, and electrode connections [30]. TGAM1 EEG Sensor uses a main electrode called EEG signal pad made of silver, which is a good conductor.…”

Section: Methodsmentioning

confidence: 99%

“…TGAM1 EEG Sensor uses a main electrode called EEG signal pad made of silver, which is a good conductor. The design of this sensor holds impedance below 5 KΩ in each electrode because both the sort the electrodes' material and the dolphin's skin under salt water can reduce the impedance and increase conductivity [30,31]. Ground electrodes and their function is to make a difference between EEG signal pad and the ambient noise.…”

Section: Methodsmentioning

confidence: 99%

Non-Parametric Evaluation Methods of the Brain Activity of a Bottlenose Dolphin during an Assisted Therapy

Escobar

Matamoros

Villar

et al. 2021

Animals

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Dolphin-Assisted Therapies (DAT) are alternative therapies aimed to reduce anxiety levels, stress relief and physical benefits. This paper is focused on measuring and analyzing dolphins brain activity when DAT is taking place in order to identify if there is any differences in female dolphin’s neuronal signal when it is interacting with control or intervention subjects, performing our research in Delfiniti, Ixtapa, Mexico facilities. We designed a wireless and portable electroencephalographic single-channel signal capture sensor to acquire and monitor the brain activity of a female bottle-nose dolphin. This EEG sensor was able to show that dolphin activity at rest is characterized by high spectral power at slow-frequencies bands. When the dolphin participated in DAT, a 23.53% increment in the 12–30 Hz frequency band was observed, but this only occurred for patients with some disease or disorder, given that 0.5–4 Hz band keeps it at 17.91% when there is a control patient. Regarding the fractal or Self-Affine Analysis, we found for all samples studied that at the beginning the dolphin’s brain activity behaved as a self-affine fractal described by a power-law until the fluctuations of voltage reached the crossovers, and after the crossovers these fluctuations left this scaling behavior. Hence, our findings validate the hypothesis that the participation in a DAT of a Patient with a certain disease or disorder modifies the usual behavior of a female bottle-nose dolphin.

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“…TGAM1 module is connected directly to a dry electrode, it has an EEG channel with three contacts: EEG, REF and GND. Moreover, it has an incorrect setting signaling that detects through the "Poor Signal" warning if communication with the user's head should be reestablished, and it is also provided with advanced filtering technology with high immunity to noise [23]. Its low power consumption is suitable for portable battery-powered applications, since its maximum power consumption is 15 mA at 3.3 V. On the other hand, a Bluetooth connection is used for data transmission to 57600 bauds [24].…”

Section: Theoretical Basismentioning

confidence: 99%

Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing

Escobar

Matamoros

Tejeida-Padilla

et al. 2020

Sensors

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There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients’ life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephalograms (EEG). EEG data-processing applications have been used in neuroscience research to be highly computing- and data-intensive. Our proposal is an integrated system of Electroencephalographic, Electrocardiographic, Bioacoustic, and Digital Image Acquisition Analysis to provide neuroscience experts with tools to estimate the efficiency of a great variety of therapies. The three main axes of this proposal are: parallel or distributed capture, filtering and adaptation of biomedical signals, and synchronization in real epochs of sampling. Thus, the present proposal underlies a general system, whose main objective is to be a wireless benchmark in the field. In this way, this proposal could acquire and give some analysis tools for biomedical signals used for measuring brain interactions when it is stimulated by an external system during therapies, for example. Therefore, this system supports extreme environmental conditions, when necessary, which broadens the spectrum of its applications. In addition, in this proposal sensors could be added or eliminated depending on the needs of the research, generating a wide range of configuration limited by the number of CPU cores, i.e., the more biosensors, the more CPU cores will be required. To validate the proposed integrated system, it is used in a Dolphin-Assisted Therapy in patients with Infantile Cerebral Palsy and Obsessive–Compulsive Disorder, as well as with a neurotypical one. Event synchronization of sample periods helped isolate the same therapy stimulus and allowed it to be analyzed by tools such as the Power Spectrum or the Fractal Geometry.

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Single channel brain-computer interface control system based on TGAM module

Cited by 9 publications

References 6 publications

Attention Optimization Method for EEG via the TGAM

Attention Optimization Method for EEG via the TGAM

Non-Parametric Evaluation Methods of the Brain Activity of a Bottlenose Dolphin during an Assisted Therapy

Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing

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