A review of the commercial brain-computer interface technology from perspective of industrial robotics

Zhang, Biao; Wang, Jian‐Jun; Fuhlbrigge, Thomas

doi:10.1109/ical.2010.5585311

Cited by 56 publications

(30 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Undoubtedly, present imaging techniques in general are bound to offer higher temporal and spatial resolution as their design develops over time, but what makes NIRS imaging an interesting contender is the combination of the previously mentioned factors which allows it to be a suitable device for long-term cortical activity monitoring. NIRS promises a device that can be used anywhere, inside or outside of a lab or hospital setting and that can register cortical activation throughout different activities with varying degrees of freedom without particular concern towards the subject's age group or physical condition which can have important reallife applications today [5][6][7][8]. Nevertheless, for NIRS to achieve its full potential the topic of its interface is yet to be properly addressed and designed.…”

Section: Introductionmentioning

confidence: 99%

The NIRS Cap: Key Part of Emerging Wearable Brain-Device Interfaces

Kassab¹

2017

Developments in Near-Infrared Spectroscopy

View full text Add to dashboard Cite

Nowadays, near-infrared spectroscopy (NIRS) fills a niche in medical imaging due to various reasons including non-invasiveness and portability. The special characteristics of NIRS imaging make it suitable to handle topics that were only approachable using electroencephalography (EEG) such as imaging infants and children; or studying the human brain activity during actions, like walking and drawing that require a certain amount of freedom that non-portable devices such as magnetic resonance imaging (MRI) cannot permit. This chapter discusses the unique advantages of NIRS as a functional imaging method and the main obstacles that still prevent this technology from becoming a prominent medical imaging tool. In particular, in this chapter we focus on the design of the brain-device interface: the NIRS cap and its important role in the imaging process.

show abstract

Section: Introductionmentioning

confidence: 99%

The NIRS Cap: Key Part of Emerging Wearable Brain-Device Interfaces

Kassab¹

2017

Developments in Near-Infrared Spectroscopy

View full text Add to dashboard Cite

show abstract

“…The sensing device consists of a cap with electrodes placed to the International 10-20 standard [2], [3].The amplifier can be one of numerous biological amplifiers on the market [4]. The filter and control system applied to the brain signals is the focus of BCI research.…”

Section: Introductionmentioning

confidence: 99%

A survey of brain computer interfaces and their applications

Major

Conrad

2014

Ieee Southeastcon 2014

View full text Add to dashboard Cite

This paper concerns the theory behind developing a brain computer interface (BCI) and the applications of such a system. Signal acquisition methods such as Functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS), Magnetoencephalography (MEG), Electrocorticography (ECoG), and Electroencephalography (EEG) are discussed. There is also a review of the different types of Event Related Potentials (ERP) and signal extraction methods for generating filters from the captured data to generate a model for a BCI. Finally, this paper covers a review of notable BCIs that are being utilized in a wide range of applications and gives a working example using BCILAB to generate results from a sample data set using the techniques discussed in the paper.

show abstract

“…In previous studies, EEG measurement methods using a wearable device were classified into two categories: headset-based and electrode-cap-based methods [3]. The former uses a headset-type device mounted on the user's head [4,5,8,10], and the latter uses a swimming-cap-type EEG acquisition device [7,9,11].…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

Bang

Choi

Park

2013

Sensors

View full text Add to dashboard Cite

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods.

show abstract

A review of the commercial brain-computer interface technology from perspective of industrial robotics

Cited by 56 publications

References 18 publications

The NIRS Cap: Key Part of Emerging Wearable Brain-Device Interfaces

The NIRS Cap: Key Part of Emerging Wearable Brain-Device Interfaces

A survey of brain computer interfaces and their applications

Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

Contact Info

Product

Resources

About