Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching

Shi, Tianwei; Chang, Guang-Ming; Qiang, Jiao-Feng; Liu, Ren; Cui, Wenhua

doi:10.1016/j.bspc.2022.104114

Cited by 11 publications

(20 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Al-Saegh et al [18] Some other studies, e.g., [9,12,16,25], have sought to increase the performance of MI-EEG systems using different STFT-based approaches. In these studies, STFT was generally used with different techniques, e.g., common spatial patterns or spectrum fusion methods.…”

Section: Short-time Fourier Transform-based Methodsmentioning

confidence: 99%

“…Shi et al [9] investigated a BCI that uses monocular vision and MI-EEG for unmanned aerial vehicle (UAV) indoor space target searching. In this BCI, the navigation subsystem offers the precise, 3D-space-feasible flying direction to the decision subsystem.…”

Section: Short-time Fourier Transform-based Methodsmentioning

confidence: 99%

“…For instance, Al-Saegh et al [18] attained augmentation by cutting specific time segments from the two trials, which may belong to the same or two different subjects, and then concatenating those separated time segments. Shi et al [9] flipped and translated the spectrogram's rows and columns for augmentation. However, care should be taken to ensure the augmentation method suits MI-EEG signals.…”

Section: Deep Learning and Classification-related Factorsmentioning

confidence: 99%

“…The dataset involves 60 hours of data, 75 recording sessions, 201 individual EEG BCI interaction session segments, and motor imageries of the left hand, right hand, left leg, right leg, and tongue [8]. DS11 [9] is a four-class (left hand, right hand, feet, tongue) dataset. MI-EEG and monocular vision were used to realize the UAV indoor space target searching for a BCI system.…”

Section: Introductionmentioning

confidence: 99%

“…The data set consists of recordings from twelve subjects. It contains 15-channel data sampled with 250 Hz [9]. A comprehensive review of the classification and signal-toimage conversion methods is shown in Table 2.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review

Yilmaz,

Yilmaz

2023

View full text Add to dashboard Cite

Non-invasive acquisition and analysis of human brain signals play a crucial role in the development of brain-computer interfaces, enabling their widespread applicability in daily life. Motor imagery has emerged as a prominent technique for the advancement of such interfaces. While initial machine and deep learning studies have shown promising results in the context of motor imagery, several challenges remain to be addressed prior to their extensive adoption. Deep learning, renowned for its automated feature extraction and classification capabilities, has been successfully employed in various domains. Notably, recent research efforts have focused on processing and classifying motor imagery EEG signals using two-dimensional data formats, yielding noteworthy advancements. Although existing literature encompasses reviews primarily centered on machine learning or deep learning techniques, this paper uniquely emphasizes the review of methods for constructing two-dimensional image features, marking the first comprehensive exploration of this subject. In this study, we present an overview of datasets, survey a range of signal-to-image conversion methods, and discuss classification approaches. Furthermore, we comprehensively examine the current challenges and outline future directions for this research domain.

show abstract

Section: Short-time Fourier Transform-based Methodsmentioning

confidence: 99%

Section: Short-time Fourier Transform-based Methodsmentioning

confidence: 99%

Section: Deep Learning and Classification-related Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review

Yilmaz,

Yilmaz

2023

View full text Add to dashboard Cite

show abstract

Developing an expansion‐based obstacle detection using panoptic segmentation

Pirasteh,

Varshosaz,

Badrloo

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

Safe Micro Aerial Vehicle (MAV) navigation requires detecting and avoiding obstacles. For safe MAV navigation, expansion‐based algorithms are effective for detecting obstacles. However, accurate and real‐time obstacle detection is a fundamental challenge. Some traditional methods focus on extracting geometric features from images and applying geometric constraints to identify potential obstacles. Others may leverage machine learning algorithms for object detection and classification, using features such as texture, shape, and context to distinguish obstacles from background clutter. The choice of approach depends on factors such as the specific requirements of the application, the complexity of the scene, and the available computational resources. Since obstacles, in reality, take the form of objects (e.g., persons, walls, pillars, trees, automobiles, and other structures), it is preferable to represent them according to human comprehension and as objects. Therefore, the objective of this study is to reflect on the previous research and address the issues mentioned above by extracting objects from the fisheye image using a panoptic deep‐learning network. The extracted object regions are, then, used to identify obstacles with a novel area‐based expansion rate we developed in a previous study. We compared the accuracy of obstacle detection in our proposed method to the existing method when moving forward and to the right; thus, we improved it between 10% and 18%, respectively. In addition, compared with the existing method, and due to replacing a single object with multiple regions, obstacle‐detection runtime for forward and right direction is 15.71 and 25.5 times faster, respectively, and the required match points have decreased by 49% and 55%.

show abstract

Efficient space learning based on kernel trick and dimension reduction technique for multichannel motor imagery EEG signals classification

Amiri,

Omranpour

2023

Neural Comput & Applic

View full text Add to dashboard Cite

Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching

Cited by 11 publications

References 28 publications

Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review

Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review

Developing an expansion‐based obstacle detection using panoptic segmentation

Efficient space learning based on kernel trick and dimension reduction technique for multichannel motor imagery EEG signals classification

Contact Info

Product

Resources

About