Improving the P300-based brain-computer interface with transfer learning

Hou, Jiayun; Li, Yali; Liu, Hongma; Wang, Shengjin

doi:10.1109/ner.2017.8008395

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…These methods generally obtain accuracies of approximately 75%-90% in offline analyses. For online implementation purposes, several P300 spelling systems [26], [27], [28] and a robot control system [29] based on transfer learning were proposed, and accuracies of approximately 80%-90% were achieved. When a training set containing EEG signals collected from a pool of subjects was unavailable, some other studies trained their models based on a small quantity of subject-specific labeled data as well as unlabeled data recorded during use.…”

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confidence: 99%

Eliminating or Shortening the Calibration for a P300 Brain–Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study

Gao

Huang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.

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confidence: 99%

Eliminating or Shortening the Calibration for a P300 Brain–Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study

Gao

Huang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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10 years of EPOC: A scoping review of Emotiv’s portable EEG device

Williams

McArthur

Badcock

2020

Preprint

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BACKGROUND Commercially-made low-cost electroencephalography (EEG) devices have become increasingly available over the last decade. One of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research. PURPOSE The aim of this study was to chart peer-reviewed reports of Emotiv EPOC projects to provide an informed summary on the use of this device for scientific purposes. METHODS We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsychINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, clinical, signal processing, experimental research, and validation) and location of use (as indexed by the first author’s address). RESULTS We identified 382 relevant studies. The top five publishing countries were the United States (n = 35), India (n = 25), China (n = 20), Poland (n = 17), and Pakistan (n = 17). The top five publishing cities were Islamabad (n = 11), Singapore (n = 10), Cairo, Sydney, and Bandung (n = 7 each). Most of these studies used Emotiv EPOC for BCI purposes (n = 277), followed by experimental research (n = 51). Thirty-one studies were aimed at validating EPOC as an EEG device and a handful of studies used EPOC for improving EEG signal processing (n = 12) or for clinical purposes (n = 11). CONCLUSIONS In its first 10 years, Emotiv EPOC has been used around the world in diverse applications, from control of robotic limbs and wheelchairs to user authentication in security systems to identification of emotional states. Given the widespread use and breadth of applications, it is clear that researchers are embracing this technology.

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Comparison of Classifiers for the Transfer Learning of Affective Auditory P300-Based BCIs

Onishi

Nakagawa

2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Improving the P300-based brain-computer interface with transfer learning

Cited by 5 publications

References 11 publications

Eliminating or Shortening the Calibration for a P300 Brain–Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study

Eliminating or Shortening the Calibration for a P300 Brain–Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study

10 years of EPOC: A scoping review of Emotiv’s portable EEG device

Comparison of Classifiers for the Transfer Learning of Affective Auditory P300-Based BCIs

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