Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

Jeong

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2022

Self Cite

Highly sophisticated control based on a braincomputer interface (BCI) requires decoding kinematic information from brain signals. The forearm is a region of the upper limb that is often used in everyday life, but intuitive movements within the same limb have rarely been investigated in previous BCI studies. In this study, we focused on various forearm movement decoding from electroencephalography (EEG) signals using a small number of samples. Ten healthy participants took part in an experiment and performed motor execution (ME) and motor imagery (MI) of the intuitive movement tasks (Dataset I). We propose a convolutional neural network using a channel-wise variational autoencoder (CVNet) based on inter-task transfer learning. We approached that training the reconstructed ME-EEG signals together will also achieve more sufficient classification performance with only a small amount of MI-EEG signals.The proposed CVNet was validated on our own Dataset I and a public dataset, BNCI Horizon 2020 (Dataset II). The classification accuracies of various movements are confirmed to be 0.83 (±0.04) and 0.69 (±0.04) for Dataset I and II, respectively. The results show that the proposed method exhibits performance increases of approximately 0.09∼0.27 and 0.08∼0.24 compared with the conventional models for Dataset I and II, respectively. The outcomes suggest that the training model for decoding imagined movements can be performed using data from ME and a small number of data samples from MI. Hence, it is presented the feasibility of BCI learning strategies that can sufficiently learn deep learning with a few amount of calibration dataset and time only, with stable performance.

Section: Discussionmentioning

confidence: 99%

Motor Imagery Classification Using Inter-Task Transfer Learning via a Channel-Wise Variational Autoencoder-Based Convolutional Neural Network

Jeong

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2022

Self Cite

“…We designed three endogenous paradigms for increasing the degree of freedom for drone swarm control based on the conventional studies [7], [16], [17].…”

Section: B Experimental Paradigmsmentioning

confidence: 99%

“…Non-invasive BCI systems have been applied for interaction using a robotic arm [6], [7], a wheelchair [8], [9], and a speller [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…The endogenous paradigms have the advantage that users' intentions are well reflected independently of stimuli. Endogenous paradigms allow users to imagine the tasks intuitively, which occur less fatigue when performing the tasks [7], [16], [17]. Nguyen et al [2] proposed the system to control the swarm density and the shape of the formation with a 4-degree of freedom using MI and SI paradigms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms

2021 9th International Winter Conference on Brain-Computer Interface (BCI)

Jeong

Ahn

et al. 2021

Self Cite

Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artificial intelligence, there have been many developments in the drone control system. BCI characteristic that can reflect the users' intentions led to the BCI-based drone control system. When using drone swarm, we can have more advantages, such as mission diversity, than using a single drone. In particular, BCI-based drone swarm control could provide many advantages to various industries such as military service or industry disaster. BCI Paradigms consist of the exogenous and endogenous paradigms. The endogenous paradigms can operate with the users' intentions independently of any stimulus. In this study, we designed endogenous paradigms (i.e., motor imagery (MI), visual imagery (VI), and speech imagery (SI)) specialized in drone swarm control, and EEG-based various task classifications related to drone swarm control were conducted. Five subjects participated in the experiment and the performance was evaluated using the basic machine learning algorithm. The grand-averaged accuracies were 51.1% (± 8.02), 53.2% (± 3.11), and 41.9% (± 6.09) in MI, VI, and SI, respectively. Hence, we confirmed the feasibility of increasing the degree of freedom for drone swarm control using various endogenous paradigms.

“…The second dataset we considered was collected by means of braincomputer interface [73]. Specifically, the records were collected by means of 60-channel electroencephalography (EEG), 7-channel electromyography (EMG) and 4-channel electrooculography (EOG) on K C = 11 intuitive upper extremity movements from 25 participants.…”

Section: ) Multimodal Brain-computer Interface (Bci)mentioning

confidence: 99%

A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection

Marinoni¹,

Jutten²

2021

Preprint

Representing data by means of graph structures identifies one of the most valid approach to extract information in several data analysis applications. This is especially true when multimodal datasets are investigated, as records collected by means of diverse sensing strategies are taken into account and explored. Nevertheless, classic graph signal processing is based on a model for information propagation that is configured according to heat diffusion mechanism. This system provides several constraints and assumptions on the data properties that might be not valid for multimodal data analysis, especially when large scale datasets collected from heterogeneous sources are considered, so that the accuracy and robustness of the outcomes might be severely jeopardized. In this paper, we introduce a novel model for graph definition based on fluid diffusion. The proposed approach improves the ability of graph-based data analysis to take into account several issues of modern data analysis in operational scenarios, so to provide a platform for precise, versatile, and efficient understanding of the phenomena underlying the records under exam, and to fully exploit the potential provided by the diversity of the records in obtaining a thorough characterization of the data and their significance. In this work, we focus our attention to using this fluid diffusion model to drive a community detection scheme, i.e., to divide multimodal datasets into many groups according to similarity among nodes in an unsupervised fashion. Experimental results achieved by testing real multimodal datasets in diverse application scenarios show that our method is able to strongly outperform state-of-the-art schemes for community detection in multimodal data analysis.