{"title":"Motor trajectory decoding based on fMRI-based BCI — A simulation study","authors":"S. Nam, K. Kim, Dae-Shik Kim","doi":"10.1109/IWW-BCI.2013.6506641","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2013.6506641","url":null,"abstract":"Recent brain computer interface (BCI) studies using chronically implanted microelectrode array demonstrated that electro-physiological responses from primary motor cortex (M1) can be successfully used to control a robotic arm by reading subjects' intention to move their arm [1]. In order to avoid the invasiveness of electrophysiological recording, more non-invasive approaches such as EEG or fMRI was likewise proposed. However, most non-invasive BCI studies suffer from the fact that they classify brain differential activity states, rather than deciphering the actual neural responses underlying the target behavior. In this simulation study, in order to decode the brain activity states underlying the target behavior from the fMRI signals, we found the directional tuning properties, a basic functional property of neural activity in M1, at the voxel level for motor trajectory decoding, and we performed a simulation to demonstrate that it is feasible to control the robotic arm in real time based on multi-voxel patterns.","PeriodicalId":129758,"journal":{"name":"2013 International Winter Workshop on Brain-Computer Interface (BCI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131150471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neuro-driving: Automatic perception technique for upcoming emergency situations","authors":"Jeong-Woo Kim, Il-Hwa Kim, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2013.6506609","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2013.6506609","url":null,"abstract":"We propose a neuro-driving simulation framework for the detection of emergency situation under general circum-stances. Previous work on neuro-driving has been shown that neural activity related with emergency situations while driving generates a different characteristic spatio-temporal event-related potential (ERP) pattern compared to normal state [1]. In this paper, on the basis of those study, three kinds of emergency situations are designed to discriminate ERP patterns between a variety of emergencies and normal driving situation. Based on comparison analysis based on KU and BBCI dataset, it is believed that the proposed framework can be considered as a novel EEG-based automatic perception technique for upcoming emergency situations.","PeriodicalId":129758,"journal":{"name":"2013 International Winter Workshop on Brain-Computer Interface (BCI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124225003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Non-homogeneous spatial filter optimization for EEG-based brain-computer interfaces","authors":"Tae-Eui Kam, Heung-Il Suk, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2013.6506618","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2013.6506618","url":null,"abstract":"Neuronal power attenuation or enhancement in specific frequency bands over the sensorimotor cortex, called Event-Related Desynchronization (ERD) or Event-Related Synchronization (ERS), respectively, is a major phenomenon in brain activities involved in imaginary movement of body parts. However, it is known that the nature of motor imagery-related electroencephalogram (EEG) signals is non-stationary and highly variable over time and frequency. In this paper, we propose a novel method of finding a discriminative time- and frequency-dependent spatial filter, which we call ‘non-homogeneous filter.’ We adaptively select bases of spatial filters over time and frequency. By taking both temporal and spectral features of EEGs in finding a spatial filter into account it is beneficial to be able to consider non-stationarity of EEG signals. In order to consider changes of ERD/ERS patterns over the time-frequency domain, we devise a spectrally and temporally weighted classification method via statistical analysis. Our experimental results on the BCI Competition IV dataset II-a clearly presented the effectiveness of the proposed method outperforming other competing methods in the literature.","PeriodicalId":129758,"journal":{"name":"2013 International Winter Workshop on Brain-Computer Interface (BCI)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125960544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient edge detection method for anatomic feature extraction of neuro-sensory tissue image based on optical coherence tomography","authors":"Yeong-Mun Cha, Jae‐Ho Han","doi":"10.1109/IWW-BCI.2013.6506632","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2013.6506632","url":null,"abstract":"In this work, we propose a reliable and detailed edge detection method customized on characteristics of optical coherence tomography images for stable feature extraction. Using a local window holding many pixels for tracking structural tendencies, edges are detected on reliably limited areas in reduced noise effect. For detailed pixel separation between structures, the edge detection is also achieved through clustering based on Gaussian mixture model. As results, the detected edges showed less than 3-μm of average distant differences compared to edges on manually recognized images. We believe this feature extraction method will provide improved quantitative analyses in wide OCT research areas.","PeriodicalId":129758,"journal":{"name":"2013 International Winter Workshop on Brain-Computer Interface (BCI)","volume":"39 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121932445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}