A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task

Kim, Hyeong-Jin; Lee, Minji

doi:10.1109/bci48061.2020.9061634

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…Nevertheless, besides speech restoration, there are some other novel applications of imagined speech decoding that have been explored. In Kim et al ( 2020 ), researchers proposed a BCI paradigm that combined event-related potentials and imagined speech to target individual objects in a smart home environment. This was done through EEG analysis and classification using regularized linear discriminant analysis (RLDA).…”

Section: Discussion Applications and Limitations Of Previous Researchmentioning

confidence: 99%

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Lopez-Bernal

Balderas

Ponce

et al. 2022

Front. Hum. Neurosci.

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Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

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Section: Discussion Applications and Limitations Of Previous Researchmentioning

confidence: 99%

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Lopez-Bernal

Balderas

Ponce

et al. 2022

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…The control is carried out by means of icons on the control panel which translate into the appropriate devices of the building. Kim used a similar method to control a virtual building through an on/off action [78]. With an approximate application SSVEP was applied by Park, Putze and others [72,[79][80][81].…”

Section: Concept Of Using Bci Building Interface In the Smart Citymentioning

confidence: 99%

Archives of Electrical Engineering

Kawa,

Borkowski,

Rodak

2021

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The article provides an overview of Brain Computer Interface (BCI) solutions for intelligent buildings. A significant topic from the smart cities point of view. That solution could be implemented as one of the human-building interfaces. The authors presented an analysis of the use of BCI in specific building systems. The article presents an analysis of BCI solutions in the context of controlling devices/systems included in the Building Management System (BMS). The Article confirms the possibility of using this method of communication between the user and the building's central unit. Despite many confirmations of repeatable device inspections, the article presents the challenges faced by the commercialization of the solution in buildings.

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“…MI-BCI can help neuromuscular injury patients recover or replace their motor abilities [2][3][4][5]. It can also be applied to robot control, smart home applications, and entertainment [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion

Zhang

Qiu

2023

J. Neural Eng.

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Objective: A motor imagery-based brain-computer interface (MI-BCI) translates spontaneous movement intention from the brain to outside devices. MI-BCI systems based on a single modality have been widely researched in recent decades. Lately, along with the development of neuroimaging methods, multimodal MI-BCI studies that use multiple neural signals have been proposed, which are promising for enhancing the decoding accuracy of MI-BCI. Multimodal MI data contain rich common and complementary information. Effective feature representations are helpful to promote the performance of classification tasks. Thus, it is very important to explore and extract features with higher separability and robustness from the rich information in multimodal data.Approach: In this study, a five-class motor imagery experiment was designed. Electroencephalography and functional near infrared spectroscopy data were collected simultaneously. A multimodal MI decoding neural network was proposed. In this network, to enhance the feature representation, the heterogeneous data of different modalities in the spatial dimension were aligned through the proposed spatial alignment losses. Also, the multimodal features were aligned and fused in the temporal dimension by an attention-based modality fusion module.Main results and Significance: The collected dataset was analyzed from temporal, spatial and frequency perspectives. The results showed that the multimodal data contain visually separable motor imagery patterns. The experimental results show that the proposed decoding method achieved the highest decoding accuracy among the compared methods on the self-collected dataset and a public dataset. Ablation results show that each part of the proposed method is effective. Compared with single-modality decoding, the proposed method obtained 4.6% higher decoding accuracy on the self-collected dataset. This indicates that the proposed method can improve the performance of multimodal MI decoding. This study provides a new approach for capturing the rich information in multimodal MI data and enhancing multimodal MI-BCI decoding accuracy.

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A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task

Cited by 17 publications

References 37 publications

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Archives of Electrical Engineering

Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion

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