This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.
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The rapid increase of many disorders, such as stroke, amyotrophic lateral sclerosis (ALS) or various other spinal cord injuries, strongly affects the society. This results in growing need for the improvement of communication methods in order to enable quick and efficient interaction with the environment, where in some particularly difficult cases this may be the only possible communication way. Therefore Brain-Computer Interfaces (BCI) seem to be an excellent solution not only for the, above mentioned -severe cases, but also for nondisabled, healthy users. The main purpose for the research presented in this paper was to invent easy, but efficient method for the analysis of the EEG signals and its implementation for the control purpose. As the implementation of EEG signals in BCI systems has become recently more and more popular within the last few years, lots of similar solutions have been developed. The method developed by the authors of this paper presents an innovative approach in analysis of the electroencephalographic signals. The proposed method is novel not only because of its efficiency, but also because of the choice of the applied equipment. The signal processing method was implemented on an embedded platform, so all the limitations of the embedded systems had to be taken into consideration. The proposed solution also enables customisation of the analysing criteria by using a threshold function in order to enable adaptation for various specific applications. In the carried out study only signals with limited information have been processed. The invented method is based on basic mathematical operations only. Neither filtering nor sophisticated signal processing methods were used.
Abstract-This paper describes and compares two color spaces -YUV and RGB, taking into account possible human-computer interaction applications. Human perception-oriented properties are compared, including not only file size or bandwidth, but also subjective visibility of artifacts. 1700 tests on a group of 170 people were performed to describe the subjective quality of compressed YUV and RGB images. The paper shows that the use of the YUV color space for a machine vision implementation can give better subjective image quality than the RGB color space. The authors conclude that YUV is better for machine vision implementations than RGB due to the perceptual similarities to the human vision.
This paper presents a more detailed concept of Human-Robot Interaction systems architecture. One of the main differences between the proposed architecture and other ones is the methodology of information acquisition regarding the robot's interlocutor. In order to obtain as much information as possible before the actual interaction took place, a custom Internet-of-Things-based sensor subsystems connected to Smart Infrastructure was designed and implemented, in order to support the interlocutor identification and acquisition of initial interaction parameters. The Artificial Intelligence interaction framework of the developed robotic system (including humanoid Pepper with its sensors and actuators, additional local, remote and cloud computing services) is being extended with the use of custom external subsystems for additional knowledge acquisition: device-based human identification, visual identification and audio-based interlocutor localization subsystems. These subsystems were deeply introduced and evaluated in this paper, presenting the benefits of integrating them into the robotic interaction system. In this paper a more detailed analysis of one of the external subsystems-Bluetooth Human Identification Smart Subsystem-was also included. The idea, use case, and a prototype, integration of elements of Smart Infrastructure systems and the prototype implementation were performed in a small front office of the Weegree company as a decent test-bed application area.The days, when simply having a robot was impressive, are gone. The technology should be more than just presenting content. It should be truly interactive. The authors of this work evaluated methods for the purpose of measurement and influencing the Human-Robot interaction User Experience, (see Reference [1]), concluding that the robot's interaction is perceived as more natural if the robot is able to personalize its communication content towards a particular interlocutor. It became clear, that the system should be able to get as much information as possible during and before the conversation takes place [2]. One of the key aspects in a healthy conversation is emotional state recognition. All the improvements of the robots presented in this paper are aiming for natural, real integration with human customers with the implementation of Artificial Intelligence [3,4].The authors of this work have indicated extending the robot's capabilities by adding additional external wireless sensors in Reference [2], however without Smart Infrastructure. Implementation of a remote PID (Passive Infrared Detector) sensor in order to get a signal "someone is coming" seemed to be enough at that stage.In this paper an extended approach is being presented, introducing Human Identification Smart Subsystems (HISS)-a set of external sensors, which enable us to obtain much more valuable information, resulting from the communication with the Smart Infrastructure [5] or databases of the customer. The idea presented in this paper has been successfully verified and implemented and the con...
Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky–Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.
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