標(biāo)題: Titlebook: ; [打印本頁] 作者: vitamin-D 時(shí)間: 2025-3-21 16:57
書目名稱Guide to Brain-Computer Music Interfacing影響因子(影響力)
書目名稱Guide to Brain-Computer Music Interfacing影響因子(影響力)學(xué)科排名
書目名稱Guide to Brain-Computer Music Interfacing網(wǎng)絡(luò)公開度
書目名稱Guide to Brain-Computer Music Interfacing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Guide to Brain-Computer Music Interfacing被引頻次
書目名稱Guide to Brain-Computer Music Interfacing被引頻次學(xué)科排名
書目名稱Guide to Brain-Computer Music Interfacing年度引用
書目名稱Guide to Brain-Computer Music Interfacing年度引用學(xué)科排名
書目名稱Guide to Brain-Computer Music Interfacing讀者反饋
書目名稱Guide to Brain-Computer Music Interfacing讀者反饋學(xué)科排名
作者: Awning 時(shí)間: 2025-3-21 22:22 作者: 油膏 時(shí)間: 2025-3-22 04:09 作者: nonchalance 時(shí)間: 2025-3-22 06:37
Michael Thomas Teixido,Mohammad Seyyediriefly touches on alternative, but currently less used approaches. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyze EEG signals as well as to stress the key points to understand when performing such an analysis.作者: 惡名聲 時(shí)間: 2025-3-22 09:18 作者: appall 時(shí)間: 2025-3-22 13:21
,A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain–Computer Interriefly touches on alternative, but currently less used approaches. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyze EEG signals as well as to stress the key points to understand when performing such an analysis.作者: appall 時(shí)間: 2025-3-22 19:54
Variables Influencing the Sintering of MgOed on transient and steady state evoked potentials, mental tasks and motor imagery will be described. Two real-life scenarios of EEG-based BCI applications in biometrics and device control will also be briefly explored. Finally, current challenges and future trends of this technology will be summarised.作者: exophthalmos 時(shí)間: 2025-3-23 00:35 作者: 音的強(qiáng)弱 時(shí)間: 2025-3-23 02:40 作者: 樂章 時(shí)間: 2025-3-23 07:45
Sir Anthony Eden and the Suez Crisisal intelligence and neurophilosophy. We discuss ., an experimental composition for orchestra in three movements, based on the fMRI scans taken from three different people, while they listened to the second movement of Beethoven’s ..作者: 樹木中 時(shí)間: 2025-3-23 10:09
,Electroencephalogram-based Brain–Computer Interface: An Introduction,ed on transient and steady state evoked potentials, mental tasks and motor imagery will be described. Two real-life scenarios of EEG-based BCI applications in biometrics and device control will also be briefly explored. Finally, current challenges and future trends of this technology will be summarised.作者: 氣候 時(shí)間: 2025-3-23 13:57 作者: misshapen 時(shí)間: 2025-3-23 19:08
On Mapping EEG Information into Music,lex approaches with regard to how music can be affected and controlled by brainwaves. This, paralleled with developments in our understanding of brainwave activity has helped push brain–computer music interfacing into innovative realms of real-time musical performance, composition and applications for music therapy.作者: 重疊 時(shí)間: 2025-3-24 01:14 作者: 吵鬧 時(shí)間: 2025-3-24 02:24
Contemporary Approaches to Music BCI Using P300 Event Related Potentials, of their quality. Instead, what follows is a basic introduction to what ERPs are, what the P300 is, and how it can be applied in the development of these BCMI designs. This description of ERPs is not intended to be exhaustive, and at best should be thought of as an illustration designed to allow th作者: 一瞥 時(shí)間: 2025-3-24 06:55 作者: 推遲 時(shí)間: 2025-3-24 12:34 作者: 牽連 時(shí)間: 2025-3-24 16:58
Emotional Responses During Music Listening,responses to music listening are briefly discussed, and their application to emotion recognition and to emotion intelligence in human–machine interaction is described. Music processing in the brain involves different brain areas and several studies attempted to investigate brain activity in relation作者: 網(wǎng)絡(luò)添麻煩 時(shí)間: 2025-3-24 22:07 作者: 獸群 時(shí)間: 2025-3-25 02:07
Retroaction Between Music and Physiology: An Approach from the Point of View of Emotions,ween musical and physiological models. We suggest in this article an experimental real-time system aiming at studying the interactions and retroactions between music and physiology, based on the paradigm of emotions.作者: 左右連貫 時(shí)間: 2025-3-25 04:15 作者: 背書 時(shí)間: 2025-3-25 10:25 作者: 中世紀(jì) 時(shí)間: 2025-3-25 15:00
Sinterwerkstoffe aus Nickelaluminid,ter, we provide an introduction to the use of machine learning methods for identifying neural correlates of musical perception and emotion. We then provide examples of machine learning methods used to study the complex relationships between neurological activity, musical stimuli, and/or emotional re作者: 骨 時(shí)間: 2025-3-25 18:09
Ronald Younes,Nabih Nader,Georges Khouryresponses to music listening are briefly discussed, and their application to emotion recognition and to emotion intelligence in human–machine interaction is described. Music processing in the brain involves different brain areas and several studies attempted to investigate brain activity in relation作者: 得罪人 時(shí)間: 2025-3-25 20:26 作者: 聯(lián)合 時(shí)間: 2025-3-26 01:25
https://doi.org/10.1007/978-3-663-05437-5ween musical and physiological models. We suggest in this article an experimental real-time system aiming at studying the interactions and retroactions between music and physiology, based on the paradigm of emotions.作者: foreign 時(shí)間: 2025-3-26 07:53
Diffusion in Non-Stoichiometric Compoundsmanating from the brain, the design of generative music techniques that respond to such information and the definition of ways in which such technology can effectively improve the lives of people with special needs and address therapeutic needs. This chapter discusses the first two challenges, in pa作者: 嘴唇可修剪 時(shí)間: 2025-3-26 12:12
Variables Influencing the Sintering of MgO–computer interface (BCI) is a revolutionary new area using EEG that is most useful for the severely disabled individuals for hands-off device control and communication as they create a direct interface from the brain to the external environment, therefore circumventing the use of peripheral muscles作者: Scleroderma 時(shí)間: 2025-3-26 13:46
https://doi.org/10.1007/978-3-642-99398-5uter music interfaces (BCMIs). It also includes results of research in refining digital signal processing (DSP) approaches and models of interaction using low-cost, portable BCIs. We will look at a range of designs for BCMIs using ERP techniques. These include the P300 Composer, the P300 Scale Playe作者: 修改 時(shí)間: 2025-3-26 17:17 作者: 上下連貫 時(shí)間: 2025-3-27 00:08 作者: 推測 時(shí)間: 2025-3-27 02:01
Ronald Younes,Nabih Nader,Georges Khouryional models and their adequacy in describing emotional responses to music are described and discussed in different applications. The underlying emotion induction mechanisms beside cognitive appraisal are presented, and their implications on the field are analyzed. Musical characteristics such as te作者: 要素 時(shí)間: 2025-3-27 08:02 作者: Debility 時(shí)間: 2025-3-27 09:57 作者: 座右銘 時(shí)間: 2025-3-27 13:39
Raj Senani,D. R. Bhaskar,R. K. Sharma expected or required to be in a particular state of alertness. For instance, pilots, security personnel, or medical personnel are expected to be in a highly alert state, and this method could help to confirm this or detect possible problems. In this work, electroencephalographic (EEG) data from 58 作者: 揉雜 時(shí)間: 2025-3-27 21:49
https://doi.org/10.1007/978-3-319-42931-1erface (BCMI) system has recently become a realistic achievement. This chapter discusses previous research in the fields of mapping, sonification and musification in the context of designing a BCMI system and will be of particular interest to those who seek to develop their own. Design of a BCMI req作者: Capitulate 時(shí)間: 2025-3-27 23:39
https://doi.org/10.1007/978-3-663-05437-5ins a wide unexplored field of study. When one starts analyzing physiological signals measured on a person listening to music, one has to firstly define models to know what information could be observed with these signals. Conversely, when one starts trying to generate some music from physiological 作者: 討好女人 時(shí)間: 2025-3-28 05:36 作者: Stress-Fracture 時(shí)間: 2025-3-28 07:40
Keynes and the Cambridge Economics Schoolwithout the need for the user to control his brain activity. Passive BCI seem particularly relevant in the context of music creation where they can provide novel information to adapt the music creation process (e.g., user mental concentration state to adapt the music tempo). In this chapter, we pres作者: 簡略 時(shí)間: 2025-3-28 13:04 作者: 令人作嘔 時(shí)間: 2025-3-28 17:56 作者: 易改變 時(shí)間: 2025-3-28 19:37
Contemporary Approaches to Music BCI Using P300 Event Related Potentials,uter music interfaces (BCMIs). It also includes results of research in refining digital signal processing (DSP) approaches and models of interaction using low-cost, portable BCIs. We will look at a range of designs for BCMIs using ERP techniques. These include the P300 Composer, the P300 Scale Playe作者: 樸素 時(shí)間: 2025-3-28 22:55 作者: Exaggerate 時(shí)間: 2025-3-29 06:44
Machine Learning to Identify Neural Correlates of Music and Emotions,yet fully explored. Furthermore, the large number of features which may be extracted from, and used to describe, neurological data, music stimuli, and emotional responses, means that the relationships between these datasets produced during music listening tasks or the operation of a brain–computer m作者: SMART 時(shí)間: 2025-3-29 08:28
Emotional Responses During Music Listening,ional models and their adequacy in describing emotional responses to music are described and discussed in different applications. The underlying emotion induction mechanisms beside cognitive appraisal are presented, and their implications on the field are analyzed. Musical characteristics such as te作者: BAIL 時(shí)間: 2025-3-29 14:29 作者: Nmda-Receptor 時(shí)間: 2025-3-29 17:21
An Introduction to EEG Source Analysis with an Illustration of a Study on Error-Related Potentials, (EEG), other biological data, as well as in many other signal processing domains such as speech, images, geophysics, and wireless. This chapter introduces a short review of brain volume conduction theory, demonstrating that BSS modeling is grounded on current physiological knowledge. Then, it illus作者: superfluous 時(shí)間: 2025-3-29 20:25
Feature Extraction and Classification of EEG Signals. The Use of a Genetic Algorithm for an Applica expected or required to be in a particular state of alertness. For instance, pilots, security personnel, or medical personnel are expected to be in a highly alert state, and this method could help to confirm this or detect possible problems. In this work, electroencephalographic (EEG) data from 58 作者: FAWN 時(shí)間: 2025-3-30 02:16 作者: Banquet 時(shí)間: 2025-3-30 04:34
Retroaction Between Music and Physiology: An Approach from the Point of View of Emotions,ins a wide unexplored field of study. When one starts analyzing physiological signals measured on a person listening to music, one has to firstly define models to know what information could be observed with these signals. Conversely, when one starts trying to generate some music from physiological 作者: 粗野 時(shí)間: 2025-3-30 09:04 作者: WITH 時(shí)間: 2025-3-30 15:47
,Passive Brain–Computer Interfaces,without the need for the user to control his brain activity. Passive BCI seem particularly relevant in the context of music creation where they can provide novel information to adapt the music creation process (e.g., user mental concentration state to adapt the music tempo). In this chapter, we pres作者: 友好 時(shí)間: 2025-3-30 17:21 作者: addict 時(shí)間: 2025-3-31 00:23 作者: 群居男女 時(shí)間: 2025-3-31 02:24
