A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Morshed, Bashir I.

doi:10.4172/2155-9538.1000128

Cited by 26 publications

(17 citation statements)

References 101 publications

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“…At the other extreme, noninvasive scalp electrodes are used to obtain electroencepha-logram (EEG). Between these extremes lies the electrocorticogram (ECoG), typically obtained by placing macroelectrodes on the surface of the brain, widely used in epileptic patients to find the seizure focus (Lesser et al, 2010;Morshed and Khan, 2014;Yang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Electrocorticogram (ECoG) Is Highly Informative in Primate Visual Cortex

Kanth

Ray

2020

J. Neurosci.

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Neural signals recorded at different scales contain information about environment and behavior and have been used to control Brain Machine Interfaces with varying degrees of success. However, a direct comparison of their efficacy has not been possible due to different recording setups, tasks, species, etc. To address this, we implanted customized arrays having both microelectrodes and electrocorticogram (ECoG) electrodes in the primary visual cortex of 2 female macaque monkeys, and also recorded electroencephalogram (EEG), while they viewed a variety of naturalistic images and parametric gratings. Surprisingly, ECoG had higher information and decodability than all other signals. Combining a few ECoG electrodes allowed more accurate decoding than combining a much larger number of microelectrodes. Control analyses showed that higher decoding accuracy of ECoG compared with local field potential was not because of differences in low-level visual features captured by them but instead because of larger spatial summation of the ECoG. Information was high in the 30-80 Hz range and at lower frequencies. Information in different frequencies and scales was nonredundant. These results have strong implications for Brain Machine Interface applications and for study of population representation of visual stimuli.

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Section: Introductionmentioning

confidence: 99%

Electrocorticogram (ECoG) Is Highly Informative in Primate Visual Cortex

Kanth

Ray

2020

J. Neurosci.

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“…The headset can measure brainwave signals safely and easily and its Sky ThinkGear ASIC chip has been designed for health and wellness, as well as educational and popular EEG technology use. (31) According to the international 10-20 system, the NeuroSky headset is designed for use in position Fp1 to retrieve EEG signals. Position A1 is the reference ground (as shown in Fig.…”

Section: Dwt Analysis Of Eeg Signalsmentioning

confidence: 99%

Using Encephalography to Classify Mind States Associated with Cycling Activities

Chu¹,

Lin²,

Hsiao³

2020

Sensors and Materials

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Cycling is a very popular activity worldwide and cyclists often listen to music while riding. This usually involves the use of a portable music player or a cellphone, which may present safety problems. Since these devices may easily distract the attention of the cyclist. This is a matter that has not received much attention but has great relevance to traffic safety. In this study, the attention level of cyclists was measured and recorded as electroencephalograms (EEGs), and discrete wavelet transforms (DWTs) based on Daubechies wavelets were used to extract the EEG features. Six different cycling activity patterns were investigated and eigenfunctions were used to classify the attention level. After feature extraction by the DWTs, support vector machines (SVMs) and general regression neural networks (GRNNs) were employed to recognize different states of mind associated with specific cycling activities. In Case I, the recognition rates of the SVM and GRNN were used to determine the state of mind associated with two different cycling activities, riding in a straight line and riding around obstacles. In Case II, rider vigilance was investigated using the SVM and GRNN for eight different cycling scenarios. The experimental results validated the proposed method and showed the brainwave patterns to be clearly associated with different cycling activities. The experimental results showed that looking at a cellphone screen or engaging in a call caused riders to miss very obvious peripheral stimuli and the use of a cellphone by a cyclist is a clear danger to the rider and traffic safety.

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“…In addition, invasive BCI research [ 3 , 4 ] has shown straightforward advances in medicine, which can lead to innovations in BCI that are important for human beings in the future, together with other modern sciences, such as artificial intelligence and robotics. An electroencephalogram-based BCI is a non-invasive technique [ 5 ] that receives brain signals from the scalp to measure the electrical potential of the brain and converts it into commands to control electrical devices, computers, or machines without motor functions [ 6 , 7 ]. BCI systems are used in medical applications [ 8 ], such as diagnosis [ 9 , 10 , 11 , 12 ], treatment [ 13 , 14 ], and rehabilitation [ 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control

Siribunyaphat

Punsawad

2023

Sensors

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Brain–computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.

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A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Cited by 26 publications

References 101 publications

Electrocorticogram (ECoG) Is Highly Informative in Primate Visual Cortex

Electrocorticogram (ECoG) Is Highly Informative in Primate Visual Cortex

Using Encephalography to Classify Mind States Associated with Cycling Activities

Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control

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