Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals

Agarwal, Mohit; Sivakumar, R.

doi:10.1109/allerton.2019.8919795

Cited by 45 publications

(21 citation statements)

References 32 publications

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“…Such learning-based methods require user training and machine learning which also needs a lot of labelled training data. [25] proposed a fully automated unsupervised blinks detection algorithm Blink based on waveform feature detection and clusteranalysis, which do not need any user training or manual labelling requirements. However, this algorithm is focusing on the normal blink detection instead of the voluntary one.…”

Section: Related Workmentioning

confidence: 99%

“…The earlier literature [25] has found that the variability of amplitude occurs in the intra-subject cases (blinks of the same user in different time periods) and inter-subject cases(blinks from different users). Thus, it's challenging to pre-determine a fixed threshold of amplitude.…”

Section: Eye Blink Patterns Of Eeg Signalmentioning

confidence: 99%

“…The PC of the monitors is in the same LAN as the OpenBCI Cyton which collecting and sending EEG data to the OpenBCI GUI though a WiFi shield. The dataset then was processed by a normal blinks detection algorithm Blink [25], and the detection result of the subject 1 is shown in Fig. 5 (x axis: time-domain in seconds; y axis: voltagedomain in microvolt; the gray shades indicate the all detected eye blinks and their widths represent the duration of each blink).…”

Section: Eye Blink Patterns Of Eeg Signalmentioning

confidence: 99%

“…Algorithm Explanation: (1) after collecting the raw EEG data E, the first procedure is preprocessing which is to filter the high-frequent component of the raw data; (2) The first step of the algorithm is to detect the peaks of the filtered data (see Fig. 3), which are the local minimums with a threshold delta (=80μA, initially); [25], detects the blinks based on time-domain feature and cluster analysis. Firstly, the extreme points (see Fig.…”

Section: Multiple Voluntary Eye Blink Detection Algorithmmentioning

confidence: 99%

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A closed-loop brain-computer interface with augmented reality feedback for industrial human-robot collaboration

Liu

et al. 2021

Int J Adv Manuf Technol

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Industrial human-robot collaboration (HRC) aims to combine the human intelligence and robotic capability to achieve the higher productiveness. In industrial HRC, the communication between human and robot is essential to enhance the understanding the intent of each other to make a more fluent collaboration. Brain-computer interface (BCI) is a technology that could record the user's brain activity that can be translated into interaction messages (e.g., control commands) to the outside world, which is able to build a direct and efficient communication channel between human and robot. However, due to lacking of information feedback mechanism, it is challenging for BCI to control robots with high degree of freedom with the limited number of classifiable mental state.To address this problem, this paper proposes a close-loop BCI with contextual visual feedback by an augmented reality (AR) headset. In such BCI, the electroencephalogram (EEG) patterns from the multiple voluntary eye blinks are considered as the input and the its online detection algorithm is proposed whose average accuracy can reach 94.31%. Moreover, an AR-enable information feedback interface is designed which enable to achieve an interactive robotic path planning. A case study of an industrial HRC assembly task is also develop which shows that the proposed closed-up BCI could shorten the time of user input in human-robot interaction.

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Section: Related Workmentioning

confidence: 99%

Section: Eye Blink Patterns Of Eeg Signalmentioning

confidence: 99%

Section: Eye Blink Patterns Of Eeg Signalmentioning

confidence: 99%

Section: Multiple Voluntary Eye Blink Detection Algorithmmentioning

confidence: 99%

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A closed-loop brain-computer interface with augmented reality feedback for industrial human-robot collaboration

Liu

et al. 2021

Int J Adv Manuf Technol

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“…Indeed, many state-of-the-art approaches use unsupervised methods for the detection of specific artifact types under specific circumstances. For instance, the Blink algorithm described by Agarwal et al is a fully unsupervised EEG artifact detection algorithm ( 6 ) that is effective for the detection of eye-blinks. While existing methods provide excellent performance for specific artifact types, there is a need for additional progress toward generalized artifact detection approaches, that make no assumptions about the task, subject, or circumstances.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised EEG Artifact Detection and Correction

Saba-Sadiya

Alhanai

Liu

et al. 2021

Front. Digit. Health

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Electroencephalography (EEG) is used in the diagnosis, monitoring, and prognostication of many neurological ailments including seizure, coma, sleep disorders, brain injury, and behavioral abnormalities. One of the primary challenges of EEG data is its sensitivity to a breadth of non-stationary noises caused by physiological-, movement-, and equipment-related artifacts. Existing solutions to artifact detection are deficient because they require experts to manually explore and annotate data for artifact segments. Existing solutions to artifact correction or removal are deficient because they assume that the incidence and specific characteristics of artifacts are similar across both subjects and tasks (i.e., “one-size-fits-all”). In this paper, we describe a novel EEG noise-reduction method that uses representation learning to perform patient- and task-specific artifact detection and correction. More specifically, our method extracts 58 clinically relevant features and applies an ensemble of unsupervised outlier detection algorithms to identify EEG artifacts that are unique to a given task and subject. The artifact segments are then passed to a deep encoder-decoder network for unsupervised artifact correction. We compared the performance of classification models trained with and without our method and observed a 10% relative improvement in performance when using our approach. Our method provides a flexible end-to-end unsupervised framework that can be applied to novel EEG data without the need for expert supervision and can be used for a variety of clinical decision tasks, including coma prognostication and degenerative illness detection. By making our method, code, and data publicly available, our work provides a tool that is of both immediate practical utility and may also serve as an important foundation for future efforts in this domain.

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EEG Based Emotion Classification Using Xception Architecture

Phukan

Gupta

2022

Lecture Notes in Networks and Systems

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Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals

Cited by 45 publications

References 32 publications

A closed-loop brain-computer interface with augmented reality feedback for industrial human-robot collaboration

A closed-loop brain-computer interface with augmented reality feedback for industrial human-robot collaboration

Unsupervised EEG Artifact Detection and Correction

EEG Based Emotion Classification Using Xception Architecture

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