The Mixed Kernel Function SVM-Based Point Cloud Classification

Chen, Chao; Li, Xiaomin; Belkacem, Abdelkader Nasreddine; Qiao, Zhifeng; Dong, Enzeng; Tan, Wenjun; Shin, Duk

doi:10.1007/s12541-019-00102-3

Cited by 23 publications

(20 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CCA algorithm is used to analyze SSVEP, the correlation coefficients between brain signal and four kinds of brain signal are extracted and calculated, and the transmission rate and the accuracy of online experiment are calculated. Although the CCA algorithm used in this paper has great advantages in SSVEP, compared with SVM method [25], it still needs to be improved. Because the human brain has complex neural mechanisms, it may not be a simple linear transformation in the transmission of electrical signals in the brain.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

Shao

Zhang

Belkacem

et al. 2020

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots have rapidly developed and have gradually been applied to enhance the quality of people’s lives. We focus on connecting the brain with a mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer the promise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomy, mobility, and abilities. Several types of robots have been controlled using BCI systems to complete real-time simple and/or complicated tasks with high performances. In this paper, a new EEG-based intelligent teleoperation system was designed for a mobile wall-crawling cleaning robot. This robot uses crawler type instead of the traditional wheel type to be used for window or floor cleaning. For EEG-based system controlling the robot position to climb the wall and complete the tasks of cleaning, we extracted steady state visually evoked potential (SSVEP) from the collected electroencephalography (EEG) signal. The visual stimulation interface in the proposed SSVEP-based BCI was composed of four flicker pieces with different frequencies (e.g., 6 Hz, 7.5 Hz, 8.57 Hz, and 10 Hz). Seven subjects were able to smoothly control the movement directions of the cleaning robot by looking at the corresponding flicker using their brain activity. To solve the multiclass problem, thereby achieving the purpose of cleaning the wall within a short period, the canonical correlation analysis (CCA) classification algorithm had been used. Offline and online experiments were held to analyze/classify EEG signals and use them as real-time commands. The proposed system was efficient in the classification and control phases with an obtained accuracy of 89.92% and had an efficient response speed and timing with a bit rate of 22.23 bits/min. These results suggested that the proposed EEG-based clean robot system is promising for smart home control in terms of completing the tasks of cleaning the walls with efficiency, safety, and robustness.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In this paper, CCA was used for developing our signal processing algorithm. Compared with other SSVEP signal classification algorithms [10][11][12][13][14]25], CCA classification algorithm is fast, efficient, simple, and easy to use.…”

Section: Introductionmentioning

confidence: 99%

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

Shao

Zhang

Belkacem

et al. 2020

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…BCI also can be very useful for the elder people [ 28 – 31 ]. To improve BCI performance for daily-life use, advanced classification methods are required [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

The Time Course of Perceptual Closure of Incomplete Visual Objects: An Event-Related Potential Study

Liu

Sha

Zhang

et al. 2020

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Perceptual organization is an important part of visual and auditory information processing. In the case of visual occlusion, whether the loss of information in images could be recovered and thus perceptually closed affects object recognition. In particular, many elderly subjects have defects in object recognition ability, which may be closely related to the abnormalities of perceptual functions. This phenomenon even can be observed in the early stage of dementia. Therefore, studying the neural mechanism of perceptual closure and its relationship with sensory and cognitive processing is important for understanding how the human brain recognizes objects, inspiring the development of neuromorphic intelligent algorithms of object recognition. In this study, a new experiment was designed to explore the realistic process of perceptual closure under occlusion and intact conditions of faces and building. The analysis of the differences in ERP components P1, N1, and Ncl indicated that the subjective awareness of perceptual closure mainly occurs in Ncl, but incomplete information has been processed and showed different manners compared to complete stimuli in N170 for facial materials. Although occluded, faces, but not buildings, still maintain the specificity of perceptual processing. The Ncl by faces and buildings did not show significant differences in both amplitude and latency, suggesting a “completing” process regardless of categorical features.

show abstract

“…It is important to highlight that nowadays, the computer science community continues creating new kernels and methodologies for improving the existing SVM-based models [15], [23], [32], [33].…”

Section: Black-box Approachmentioning

confidence: 99%

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

2019

View full text Add to dashboard Cite

Nowadays, in the international scientific community of machine learning, there exists an enormous discussion about the use of black-box models or explainable models; especially in practical problems. On the one hand, a part of the community defends that black-box models are more accurate than explainable models in some contexts, like image preprocessing. On the other hand, there exist another part of the community alleging that explainable models are better than black-box models because they can obtain comparable results and also they can explain these results in a language close to a human expert by using patterns. In this paper, advantages and weaknesses for each approach are shown; taking into account a stateof-the-art review for both approaches, their practical applications, trends, and future challenges. This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model. Also, we propose some ideas for fusing both, explainable and blackbox, approaches to provide better solutions to experts in real-world domains. Additionally, we show one way to measure the effectiveness of the applied machine learning model by using expert opinions jointly with statistical methods. Throughout this paper, we show the impact of using explainable and black-box models on the security and medical applications. INDEX TERMS Black-box, white-box, explainable artificial intelligence, deep learning.

show abstract

The Mixed Kernel Function SVM-Based Point Cloud Classification

Cited by 23 publications

References 19 publications

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

The Time Course of Perceptual Closure of Incomplete Visual Objects: An Event-Related Potential Study

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

Contact Info

Product

Resources

About