1
|
Liu S, Kersten DJ, Legge GE. Effect of expansive optic flow and lateral motion parallax on depth estimation with normal and artificially reduced acuity. J Vis 2023; 23:3. [PMID: 37801321 PMCID: PMC10561791 DOI: 10.1167/jov.23.12.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 09/07/2023] [Indexed: 10/07/2023] Open
Abstract
When an observer moves in space, the retinal projection of a stationary object either expands if the motion is toward the object or shifts horizontally if the motion contains a lateral component. This study examined the impact of expansive optic flow and lateral motion parallax on the accuracy of depth perception for observers with normal or artificially reduced acuity and asked whether any benefit is due to the continuous motion or to the discrete object image displacement. Stationary participants viewed a virtual room on a computer screen. They used an on-screen slider to estimate the depth of a target object relative to a reference object after seeing 2-second videos simulating five conditions: static viewing, expansive optic flow, and lateral motion parallax in either continuous motion or image displacement. Ten participants viewed the stimuli with normal acuity in Experiment 1 and 11 with three levels of artificially reduced acuity in Experiment 2. Linear regression models represented the relationship between the depth estimates of participants and the ground truth. Lateral motion parallax produced more accurate depth estimates than expansive optic flow and static viewing. Depth perception with continuous motion was more accurate than that with displacement under mild and moderate, but not severe, acuity reduction. For observers with both normal and artificially reduced acuity, lateral motion parallax was more helpful for object depth estimation than expansive optic flow, and continuous motion parallax was more helpful than object image displacement.
Collapse
Affiliation(s)
- Siyun Liu
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Daniel J Kersten
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Gordon E Legge
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| |
Collapse
|
2
|
Lyu X, Ding P, Li S, Dong Y, Su L, Zhao L, Gong A, Fu Y. Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery. Cogn Neurodyn 2023; 17:105-118. [PMID: 36704636 PMCID: PMC9871150 DOI: 10.1007/s11571-022-09808-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/04/2022] [Accepted: 04/01/2022] [Indexed: 01/29/2023] Open
Abstract
Existing brain-computer interface (BCI) research has made great progress in improving the accuracy and information transfer rate (ITR) of BCI systems. However, the practicability of BCI is still difficult to achieve. One of the important reasons for this difficulty is that human factors are not fully considered in the research and development of BCI. As a result, BCI systems have not yet reached users' expectations. In this study, we investigate a BCI system of motor imagery for lower limb synchronous rehabilitation as an example. From the perspective of human factors engineering of BCI, a comprehensive evaluation method of BCI system development is proposed based on the concept of human-centered design and evaluation. Subjects' satisfaction ratings for BCI sensors, visual analog scale (VAS), subjects' satisfaction rating of the BCI system, and the mental workload rating for subjects manipulating the BCI system, as well as interview/follow-up comprehensive evaluation of motor imagery of BCI (MI-BCI) system satisfaction were used. The methods and concepts proposed in this study provide useful insights for the design of personalized MI-BCI. We expect that the human factors engineering of BCI could be applied to the design and satisfaction evaluation of MI-BCI, so as to promote the practical application of this kind of BCI.
Collapse
Affiliation(s)
- Xiaotong Lyu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, Yunnan China
| | - Peng Ding
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, Yunnan China
| | - Siyu Li
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, Yunnan China
| | - Yuyang Dong
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, Yunnan China
| | - Lei Su
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan China
| | - Lei Zhao
- Faculty of Science, Kunming University of Science and Technology, Kunming, Yunnan China
| | - Anmin Gong
- School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xian, Shanxi China
| | - Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, Yunnan China
| |
Collapse
|
3
|
A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test. Cogn Neurodyn 2020; 15:621-636. [PMID: 34367364 DOI: 10.1007/s11571-020-09646-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 09/07/2020] [Accepted: 10/21/2020] [Indexed: 10/23/2022] Open
Abstract
Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test-retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise.
Collapse
|
4
|
Daneshi A, Towhidkhah F, Faubert J. Assessing changes in brain electrical activity and functional connectivity while overtaking a vehicle. JOURNAL OF COGNITIVE PSYCHOLOGY 2020. [DOI: 10.1080/20445911.2020.1815753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Asieh Daneshi
- Biomedical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Farzad Towhidkhah
- Biomedical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Jocelyn Faubert
- Faubert Laboratory, School of Optometry, University of Montreal, Montreal, Canada
| |
Collapse
|
5
|
Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method. Cogn Neurodyn 2020; 15:207-222. [PMID: 33854640 DOI: 10.1007/s11571-020-09614-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/07/2020] [Accepted: 06/23/2020] [Indexed: 02/07/2023] Open
Abstract
Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.
Collapse
|
6
|
Wang Y, Xu X, Wang R. Energy features in spontaneous up and down oscillations. Cogn Neurodyn 2020; 15:65-75. [PMID: 33786080 DOI: 10.1007/s11571-020-09597-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/25/2020] [Accepted: 05/04/2020] [Indexed: 12/22/2022] Open
Abstract
Spontaneous brain activities consume most of the brain's energy. So if we want to understand how the brain operates, we must take into account these spontaneous activities. Up and down transitions of membrane potentials are considered to be one of significant spontaneous activities. This kind of oscillation always shows bistable and bimodal distribution of membrane potentials. Our previous theoretical studies on up and down oscillations mainly looked at the ion channel dynamics. In this paper, we focus on energy feature of spontaneous up and down transitions based on a network model and its simulation. The simulated results indicate that the energy is a robust index and distinguishable of excitatory and inhibitory neurons. Meanwhile, one the whole, energy consumption of neurons shows bistable feature and bimodal distribution as well as the membrane potential, which turns out that the indicator of energy consumption encodes up and down states in this spontaneous activity. In detail, energy consumption mainly occurs during up states temporally, and mostly concentrates inside neurons rather than synapses spatially. The stimulation related energy is small, indicating that energy consumption is not driven by external stimulus, but internal spontaneous activity. This point of view is also consistent with brain imaging results. Through the observation and analysis of the findings, we prove the validity of the model again, and we can further explore the energy mechanism of more spontaneous activities.
Collapse
Affiliation(s)
- Yihong Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Xuying Xu
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Rubin Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.,School of Computer Science, Hangzhou Dianzi University, Hangzhou, China
| |
Collapse
|