In summary, the incidence of BCI following blunt thoracic trauma patients has been reported between 20% and 76%, and no gold standard exists to diagnose BCI. Diagnostic tests should be limited to identify those patients who are at risk of developing cardiac complications as a result of BCI. Therapeutic interventions should be directed to treat the complications of BCI. Finally, the prognosis an...

OBJECTIVES We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP)-based Brain-Computer Interfaces (BCIs) for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS). METHODS Seven patients performed repeated sessions with each BCI. We assessed BCI performance, mental workload and overall satisfaction for both systems...

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control external devices, due its portability, high temporal resolution, ease of use low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system uses multiple stimuli (such as LEDs or boxes on a computer scre...

Purpose: Previous results from the TransATAC study demonstrated that both the Breast Cancer Index (BCI) and the OncotypeDX Recurrence Score (RS) added significant prognostic information to clinicopathologic factors over a 10-year period. Here, we examined cross-stratification between BCI and RS to directly compare their prognostic accuracy at the individual patient level. Experimental Design: A...

In this paper, we analyze the performance of Time Delay Neural Networks (TDNN) and Hidden Markov Models (HMM) for Electroencephalogram (EEG) signal classification. The specific focus of this study is Brain-Computer Interfacing (BCI), where near-real time detection of mental commands during a multi-channel EEG recording is desired. We argue that HMM and TDNN should be preferred over the rigid, o...

Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired vol...

Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4th International BCI conference, which took place in May-June 2010 in Asilomar, California. We assessed respondents' opinions about a number of to...

Due to the non-stationarity of EEG signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Asynchronous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user’s control intention and timing are usually unknown. This paper proposes a novel motor imagery based a...

(E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI) is known as a dual specific phosphatase 1/6 or MAPK inhibitor. However, its precise anti-lung cancer mechanism remains unknown. In this study, the effects of BCI on cell viability were investigated in the non-small cell lung cancer cell lines NCIH1299, A549, and NCI-H460. We confirmed that BCI significantly inhibited the cel...