PMC:2944670 / 43759-55988 JSONTXT

{"project":"TEST0","denotations":[{"id":"20877434-231-239-476029","span":{"begin":1822,"end":1826},"obj":"[\"19635654\"]"},{"id":"20877434-196-204-476030","span":{"begin":2642,"end":2646},"obj":"[\"19635654\"]"},{"id":"20877434-90-98-476032","span":{"begin":7151,"end":7155},"obj":"[\"18270004\"]"},{"id":"20877434-48-56-476033","span":{"begin":7400,"end":7404},"obj":"[\"12899249\"]"},{"id":"20877434-114-122-476035","span":{"begin":12027,"end":12031},"obj":"[\"16792287\"]"}],"text":"Entertainment\nThe area of entertainment has typically had a lower priority in BCI work, compared to more “functional” activities such as basic communication or control tasks. For the purposes of this survey, entertainment encompasses everything from video games, to interaction with collections of media to control of ambient features, such as wall displays, lighting, and music. In tasks such as music or images, the feedback from even a “wrong” selection is usually pleasant (assuming the user likes the music or images in their collection), and interaction techniques can be focused on more exploratory approaches to browsing collections. This is sometimes called hedonic interaction in distinction from utilitarian interaction, and it leads to a need for a more broad set of metrics for evaluation of user experience. In this context, a BCI will facilitate activities such as browsing digital photo collections or music collections, where the control might be at the level of specifying a mood or genre. Such systems might also provide opportunities for users to express their emotional state, or desires to a caregiver more rapidly and expressively than using written language.\nAs an example of this BCI approach to entertainment, very recent work has begun to gather experience with synchronous and asynchronous BCI “painting” applications which allow the user creative expression. Preliminary results indicate that the application provides pleasure to patients, healthy volunteers, and artists (Kübler et al., 2008; Halder et al., 2009).\n\nGaming\nAlthough gaming has not been the main focus of BCI research, there exist some prototypes that demonstrate the feasibility of games controlled by a BCI (Millán, 2003; Lalor et al., 2005; Krepki et al., 2007; Nijholt et al., 2008b; Tangermann et al., 2008; Finke et al., 2009; Nijholt, 2009). Such BCI games could allow severely disabled persons to not only experience a little bit of entertainment, but to also to improve their quality of life, mainly through social interaction. For instance, Tangermann et al. (2008) shows evidence that real-time BCI control of a physical game machine is possible with little subject training. The gaming machine studied (a standard pinball machine) required only two classes for control with fast and precise reaction; predictive behavior and learning are mandatory. Games can be either competitive (requiring fast responses) or strategic (usually slower).\nThese BCI games are based on different BCI protocols, from spontaneous EEG (Millán, 2003; Krepki et al., 2007; Tangermann et al., 2008) to evoked EEG potentials (Lalor et al., 2005; Finke et al., 2009), where the user delivers (as usual for a BCI) mental commands to control some aspect of the game. Another alternative is to determine the user's mental or affective state from their EEG and to use this information to adapt the dynamics of the game to the user's affective state (Nijholt et al., 2008b). As stated in Nijholt et al. (2008a), “Measuring brain activity for gamers can be used so that the game environment (1) knows what a subject experiences and can adapt game and interface in order to keep the gamer “in the flow” of the game, and (2) allows the gamer to add brain control commands to the already available control commands for the game.” This perspective matches well that described in Williamson (2006) when discussing a general framework for interaction design.\nIt is usually assumed that, because of the huge yearly turnovers of the game industry, once BCI games reach the mass market, BCI technology would become so cheap that every disabled person would be able to afford it for functional interaction. Some support this view. For instance, commercial “BCI” sensors are coming into the mainstream gaming world (e.g., Emotiv and Neurosky). Also, as Nijholt (2009) points out: “There are also other reasons that make games, gamers and the game industry interesting. Gamers are early adaptors. They are quite happy to play with technology, to accept that strong efforts have to be made in order to get minimal advantage, and they are used to the fact that games have to be mastered by training, allowing them to go from one level to the next level and to get a higher ranking than their competitors”. However, we cannot take for granted that the kind of BCI technology (sensors and brain signals) that the game industry would eventually develop will automatically be appropriate for functional interaction. This is the case for current “BCI” game sensors that are limited in number and position over the users head (normally just over the forefront, where there is no hair).\nOne concern with the mass-produced BCI games is proper evaluation; namely, how to prove that the user's brainwaves are the actual control signals driving the game. Of course, from a hybrid BCI perspective, gamers can (and must) also use other physiological signals and interaction modalities. The point, however, is to demonstrate that users have a sufficient degree of mental control for those aspects of the game that require so, as advertised. This issue also raises the question of how to evaluate games as a whole to ensure that they provide a valuable and enjoyable experience. In this respect, the Fun of Gaming (FUGA) project advocates a multi-dimensional evaluation using self-reports, behavioral observations and psychophysiological measures as each in itself is insufficient to get the full picture (IJsselsteijn et al., 2008). Much of the research in pleasure and satisfaction in entertainment focuses on gaming but some might be applied to entertainment in general. For example, “fun” in a game includes challenge, curiosity, fantasy, and Csikszentmihalyi's theory of flow (level of engagement that one is completely absorbed in the current activity and enjoys it in itself without any need for future benefit), but these can also apply to interactive art and creativity (and by extension interactive media, Costello and Edmonds, 2007). Only such a kind of evaluation will prove beneficial for BCI games in general, and for disabled people in particular. Otherwise, BCI games will be just another “fast-food toy” that customers buy and stop using quickly, thus risking to seriously damage the credibility of the BCI field – such a blow that early in its development stage could cripple the field, by projecting a negative image to the public, other industrial sectors, and to funding agencies.\n\nVirtual reality\nBecause BCI are a closed-loop systems, feedback is an important component. Various methods of providing feedback can inform the participant about success or failure of an intended act. Thus, feedback either supports reinforcement during the learning/training process or in controlling the application. In particular, the use of virtual reality (VR) has been proven to be an interesting and promising way to realize such feedback.\nSeveral prototypes have enabled users to navigate in virtual scenes solely by means of their oscillatory cerebral activity, recorded on the scalp via EEG electrodes. Healthy participants were exploring virtual spaces (Leeb et al., 2007b,c; Scherer et al., 2008; Ron-Angevin et al., 2009), were manipulating virtual objects (Lecuyer et al., 2008), and a spinal-cord injured patient was controlling a wheelchair through a virtual street (Leeb et al., 2007a). Additionally, evoked potentials (P300, Bayliss, 2003; and SSVEPs, Lalor et al., 2005) have been used to control VR feedback as well. In these studies, BCI users who use immersive Virtual Environments (VEs) make fewer errors, report that BCIs are easier to learn and use, and state that they enjoy BCI use more (Leeb et al., 2006, 2007b; Ron-Angevin et al., 2009). These benefits may occur because VEs enhance vividness and mental effort, which may lead to more distinct brain patterns and improve pattern recognition performance. Nevertheless, VR technologies provide motivating, safe, and controlled conditions that enable improvement of BCI learning as well as the investigation of the brain responses and neural processes involved, meanwhile testing new virtual prototypes.\n\nMusic browsing\nSince the introduction of mp3 compression technology and easy-to-use mobile music players (such as Apple's iPod player, and iTunes software), there has been an explosion in the use of computers for listening to music. For example, listeners can create “playlists” of their favorite tracks to listen to, burn tracks to CDs, or share with friends. In many cases, though, typical users find that this requires too much effort. Recently a lot of publicity has been given to the “Genius” feature on Apple's widely used iTunes software, although a range of alternatives have been in existence for some time (e.g., websites such as last.fm, pandora.com, www.spotify.com). Moodplayer is an application that lets you create playlists on the go based on your mood and the mood of songs in your music library, and which can be installed on iPhones or Nokia phones. This is a natural application area for BCI. Although no BCI music browser has been developed yet, some BCI for music composition (Miranda, 2006) do exist.\n\nPhoto browsing\nExisting research has focused on determining what kinds of photographs people have, what tasks they perform with them (and what tasks they would like to perform but cannot), and what structure the collections have. In particular, Frohlich et al. (2002) and Kirk et al. (2006) examine how users utilize their personal digital photographs. Both noted the general lack of organization of digital photographs, and the use of very simple exploration techniques. Complex searching activities were not found to be of particular benefit to users when dealing with their personal archives. Rodden and Wood (2003) also examined digital photograph activities, observing a distinct lack of annotation activity and the utility of temporal structuring in exploration of photo archives. An individual picking up an interactive photo display often does not have a clear idea of what images he or she wishes to see. This partially explains why many sophisticated and powerful organization and query interfaces are not widely adopted. Few users know what they want to see before they begin; fewer still are able to distill those intents into meaningful queries across the attributes of images which the system observes. Photo journalists, archivists or other workers with very specific and well-defined needs may benefit from such interactions. This use case, however, is exceedingly rare among home users exploring personal photograph collections.\nAlthough users may not have a definite idea of what images they would be interested in seeing, or are unable to communicate their preferences given the available metadata attributes, they may instead be able to iteratively refine selections to find images of interest. The presentation of a sample from a large set of images can stimulate memories; user can then follow paths through photo space by indicating that they would like to see more images “similar” to one or more of those displayed. Using rich similarity metrics is essential in obtaining effective navigation by this means. This style of interaction has much in common with Bates’ “berry picking” model of information retrieval (Bates, 1989). In this model, users wander through an information space, finding results and modifying their queries as they go. The final goal of the user adapts as they bounce through the results from each previous query. This approach is well-suited to develop BCI tools for photo browsing. The idea is to combine BCI with simple image search techniques. Users will mentally select pictures representing possible categories in their photo archives with a P300-based BCI, and image search techniques will provide similar pictures. In fact, there is some preliminary work that follow this P300 approach (Touyama, 2008). Also of interest is the use of rapid serial visual presentation (RSVP) paradigms for image triage (Gerson et al., 2006). In this approach, users watch many images presented a high rate (say, 4 Hz) and the presence of a P300 evoked potential indicates images of interest that are ranked on top of the final selection."}

{"project":"0_colil","denotations":[{"id":"20877434-19635654-476029","span":{"begin":1822,"end":1826},"obj":"19635654"},{"id":"20877434-19635654-476030","span":{"begin":2642,"end":2646},"obj":"19635654"},{"id":"20877434-18270004-476032","span":{"begin":7151,"end":7155},"obj":"18270004"},{"id":"20877434-12899249-476033","span":{"begin":7400,"end":7404},"obj":"12899249"},{"id":"20877434-16792287-476035","span":{"begin":12027,"end":12031},"obj":"16792287"}],"text":"Entertainment\nThe area of entertainment has typically had a lower priority in BCI work, compared to more “functional” activities such as basic communication or control tasks. For the purposes of this survey, entertainment encompasses everything from video games, to interaction with collections of media to control of ambient features, such as wall displays, lighting, and music. In tasks such as music or images, the feedback from even a “wrong” selection is usually pleasant (assuming the user likes the music or images in their collection), and interaction techniques can be focused on more exploratory approaches to browsing collections. This is sometimes called hedonic interaction in distinction from utilitarian interaction, and it leads to a need for a more broad set of metrics for evaluation of user experience. In this context, a BCI will facilitate activities such as browsing digital photo collections or music collections, where the control might be at the level of specifying a mood or genre. Such systems might also provide opportunities for users to express their emotional state, or desires to a caregiver more rapidly and expressively than using written language.\nAs an example of this BCI approach to entertainment, very recent work has begun to gather experience with synchronous and asynchronous BCI “painting” applications which allow the user creative expression. Preliminary results indicate that the application provides pleasure to patients, healthy volunteers, and artists (Kübler et al., 2008; Halder et al., 2009).\n\nGaming\nAlthough gaming has not been the main focus of BCI research, there exist some prototypes that demonstrate the feasibility of games controlled by a BCI (Millán, 2003; Lalor et al., 2005; Krepki et al., 2007; Nijholt et al., 2008b; Tangermann et al., 2008; Finke et al., 2009; Nijholt, 2009). Such BCI games could allow severely disabled persons to not only experience a little bit of entertainment, but to also to improve their quality of life, mainly through social interaction. For instance, Tangermann et al. (2008) shows evidence that real-time BCI control of a physical game machine is possible with little subject training. The gaming machine studied (a standard pinball machine) required only two classes for control with fast and precise reaction; predictive behavior and learning are mandatory. Games can be either competitive (requiring fast responses) or strategic (usually slower).\nThese BCI games are based on different BCI protocols, from spontaneous EEG (Millán, 2003; Krepki et al., 2007; Tangermann et al., 2008) to evoked EEG potentials (Lalor et al., 2005; Finke et al., 2009), where the user delivers (as usual for a BCI) mental commands to control some aspect of the game. Another alternative is to determine the user's mental or affective state from their EEG and to use this information to adapt the dynamics of the game to the user's affective state (Nijholt et al., 2008b). As stated in Nijholt et al. (2008a), “Measuring brain activity for gamers can be used so that the game environment (1) knows what a subject experiences and can adapt game and interface in order to keep the gamer “in the flow” of the game, and (2) allows the gamer to add brain control commands to the already available control commands for the game.” This perspective matches well that described in Williamson (2006) when discussing a general framework for interaction design.\nIt is usually assumed that, because of the huge yearly turnovers of the game industry, once BCI games reach the mass market, BCI technology would become so cheap that every disabled person would be able to afford it for functional interaction. Some support this view. For instance, commercial “BCI” sensors are coming into the mainstream gaming world (e.g., Emotiv and Neurosky). Also, as Nijholt (2009) points out: “There are also other reasons that make games, gamers and the game industry interesting. Gamers are early adaptors. They are quite happy to play with technology, to accept that strong efforts have to be made in order to get minimal advantage, and they are used to the fact that games have to be mastered by training, allowing them to go from one level to the next level and to get a higher ranking than their competitors”. However, we cannot take for granted that the kind of BCI technology (sensors and brain signals) that the game industry would eventually develop will automatically be appropriate for functional interaction. This is the case for current “BCI” game sensors that are limited in number and position over the users head (normally just over the forefront, where there is no hair).\nOne concern with the mass-produced BCI games is proper evaluation; namely, how to prove that the user's brainwaves are the actual control signals driving the game. Of course, from a hybrid BCI perspective, gamers can (and must) also use other physiological signals and interaction modalities. The point, however, is to demonstrate that users have a sufficient degree of mental control for those aspects of the game that require so, as advertised. This issue also raises the question of how to evaluate games as a whole to ensure that they provide a valuable and enjoyable experience. In this respect, the Fun of Gaming (FUGA) project advocates a multi-dimensional evaluation using self-reports, behavioral observations and psychophysiological measures as each in itself is insufficient to get the full picture (IJsselsteijn et al., 2008). Much of the research in pleasure and satisfaction in entertainment focuses on gaming but some might be applied to entertainment in general. For example, “fun” in a game includes challenge, curiosity, fantasy, and Csikszentmihalyi's theory of flow (level of engagement that one is completely absorbed in the current activity and enjoys it in itself without any need for future benefit), but these can also apply to interactive art and creativity (and by extension interactive media, Costello and Edmonds, 2007). Only such a kind of evaluation will prove beneficial for BCI games in general, and for disabled people in particular. Otherwise, BCI games will be just another “fast-food toy” that customers buy and stop using quickly, thus risking to seriously damage the credibility of the BCI field – such a blow that early in its development stage could cripple the field, by projecting a negative image to the public, other industrial sectors, and to funding agencies.\n\nVirtual reality\nBecause BCI are a closed-loop systems, feedback is an important component. Various methods of providing feedback can inform the participant about success or failure of an intended act. Thus, feedback either supports reinforcement during the learning/training process or in controlling the application. In particular, the use of virtual reality (VR) has been proven to be an interesting and promising way to realize such feedback.\nSeveral prototypes have enabled users to navigate in virtual scenes solely by means of their oscillatory cerebral activity, recorded on the scalp via EEG electrodes. Healthy participants were exploring virtual spaces (Leeb et al., 2007b,c; Scherer et al., 2008; Ron-Angevin et al., 2009), were manipulating virtual objects (Lecuyer et al., 2008), and a spinal-cord injured patient was controlling a wheelchair through a virtual street (Leeb et al., 2007a). Additionally, evoked potentials (P300, Bayliss, 2003; and SSVEPs, Lalor et al., 2005) have been used to control VR feedback as well. In these studies, BCI users who use immersive Virtual Environments (VEs) make fewer errors, report that BCIs are easier to learn and use, and state that they enjoy BCI use more (Leeb et al., 2006, 2007b; Ron-Angevin et al., 2009). These benefits may occur because VEs enhance vividness and mental effort, which may lead to more distinct brain patterns and improve pattern recognition performance. Nevertheless, VR technologies provide motivating, safe, and controlled conditions that enable improvement of BCI learning as well as the investigation of the brain responses and neural processes involved, meanwhile testing new virtual prototypes.\n\nMusic browsing\nSince the introduction of mp3 compression technology and easy-to-use mobile music players (such as Apple's iPod player, and iTunes software), there has been an explosion in the use of computers for listening to music. For example, listeners can create “playlists” of their favorite tracks to listen to, burn tracks to CDs, or share with friends. In many cases, though, typical users find that this requires too much effort. Recently a lot of publicity has been given to the “Genius” feature on Apple's widely used iTunes software, although a range of alternatives have been in existence for some time (e.g., websites such as last.fm, pandora.com, www.spotify.com). Moodplayer is an application that lets you create playlists on the go based on your mood and the mood of songs in your music library, and which can be installed on iPhones or Nokia phones. This is a natural application area for BCI. Although no BCI music browser has been developed yet, some BCI for music composition (Miranda, 2006) do exist.\n\nPhoto browsing\nExisting research has focused on determining what kinds of photographs people have, what tasks they perform with them (and what tasks they would like to perform but cannot), and what structure the collections have. In particular, Frohlich et al. (2002) and Kirk et al. (2006) examine how users utilize their personal digital photographs. Both noted the general lack of organization of digital photographs, and the use of very simple exploration techniques. Complex searching activities were not found to be of particular benefit to users when dealing with their personal archives. Rodden and Wood (2003) also examined digital photograph activities, observing a distinct lack of annotation activity and the utility of temporal structuring in exploration of photo archives. An individual picking up an interactive photo display often does not have a clear idea of what images he or she wishes to see. This partially explains why many sophisticated and powerful organization and query interfaces are not widely adopted. Few users know what they want to see before they begin; fewer still are able to distill those intents into meaningful queries across the attributes of images which the system observes. Photo journalists, archivists or other workers with very specific and well-defined needs may benefit from such interactions. This use case, however, is exceedingly rare among home users exploring personal photograph collections.\nAlthough users may not have a definite idea of what images they would be interested in seeing, or are unable to communicate their preferences given the available metadata attributes, they may instead be able to iteratively refine selections to find images of interest. The presentation of a sample from a large set of images can stimulate memories; user can then follow paths through photo space by indicating that they would like to see more images “similar” to one or more of those displayed. Using rich similarity metrics is essential in obtaining effective navigation by this means. This style of interaction has much in common with Bates’ “berry picking” model of information retrieval (Bates, 1989). In this model, users wander through an information space, finding results and modifying their queries as they go. The final goal of the user adapts as they bounce through the results from each previous query. This approach is well-suited to develop BCI tools for photo browsing. The idea is to combine BCI with simple image search techniques. Users will mentally select pictures representing possible categories in their photo archives with a P300-based BCI, and image search techniques will provide similar pictures. In fact, there is some preliminary work that follow this P300 approach (Touyama, 2008). Also of interest is the use of rapid serial visual presentation (RSVP) paradigms for image triage (Gerson et al., 2006). In this approach, users watch many images presented a high rate (say, 4 Hz) and the presence of a P300 evoked potential indicates images of interest that are ranked on top of the final selection."}

{"project":"2_test","denotations":[{"id":"20877434-19635654-38387012","span":{"begin":1822,"end":1826},"obj":"19635654"},{"id":"20877434-19635654-38387013","span":{"begin":2642,"end":2646},"obj":"19635654"},{"id":"20877434-18270004-38387015","span":{"begin":7151,"end":7155},"obj":"18270004"},{"id":"20877434-12899249-38387016","span":{"begin":7400,"end":7404},"obj":"12899249"},{"id":"20877434-16792287-38387018","span":{"begin":12027,"end":12031},"obj":"16792287"}],"text":"Entertainment\nThe area of entertainment has typically had a lower priority in BCI work, compared to more “functional” activities such as basic communication or control tasks. For the purposes of this survey, entertainment encompasses everything from video games, to interaction with collections of media to control of ambient features, such as wall displays, lighting, and music. In tasks such as music or images, the feedback from even a “wrong” selection is usually pleasant (assuming the user likes the music or images in their collection), and interaction techniques can be focused on more exploratory approaches to browsing collections. This is sometimes called hedonic interaction in distinction from utilitarian interaction, and it leads to a need for a more broad set of metrics for evaluation of user experience. In this context, a BCI will facilitate activities such as browsing digital photo collections or music collections, where the control might be at the level of specifying a mood or genre. Such systems might also provide opportunities for users to express their emotional state, or desires to a caregiver more rapidly and expressively than using written language.\nAs an example of this BCI approach to entertainment, very recent work has begun to gather experience with synchronous and asynchronous BCI “painting” applications which allow the user creative expression. Preliminary results indicate that the application provides pleasure to patients, healthy volunteers, and artists (Kübler et al., 2008; Halder et al., 2009).\n\nGaming\nAlthough gaming has not been the main focus of BCI research, there exist some prototypes that demonstrate the feasibility of games controlled by a BCI (Millán, 2003; Lalor et al., 2005; Krepki et al., 2007; Nijholt et al., 2008b; Tangermann et al., 2008; Finke et al., 2009; Nijholt, 2009). Such BCI games could allow severely disabled persons to not only experience a little bit of entertainment, but to also to improve their quality of life, mainly through social interaction. For instance, Tangermann et al. (2008) shows evidence that real-time BCI control of a physical game machine is possible with little subject training. The gaming machine studied (a standard pinball machine) required only two classes for control with fast and precise reaction; predictive behavior and learning are mandatory. Games can be either competitive (requiring fast responses) or strategic (usually slower).\nThese BCI games are based on different BCI protocols, from spontaneous EEG (Millán, 2003; Krepki et al., 2007; Tangermann et al., 2008) to evoked EEG potentials (Lalor et al., 2005; Finke et al., 2009), where the user delivers (as usual for a BCI) mental commands to control some aspect of the game. Another alternative is to determine the user's mental or affective state from their EEG and to use this information to adapt the dynamics of the game to the user's affective state (Nijholt et al., 2008b). As stated in Nijholt et al. (2008a), “Measuring brain activity for gamers can be used so that the game environment (1) knows what a subject experiences and can adapt game and interface in order to keep the gamer “in the flow” of the game, and (2) allows the gamer to add brain control commands to the already available control commands for the game.” This perspective matches well that described in Williamson (2006) when discussing a general framework for interaction design.\nIt is usually assumed that, because of the huge yearly turnovers of the game industry, once BCI games reach the mass market, BCI technology would become so cheap that every disabled person would be able to afford it for functional interaction. Some support this view. For instance, commercial “BCI” sensors are coming into the mainstream gaming world (e.g., Emotiv and Neurosky). Also, as Nijholt (2009) points out: “There are also other reasons that make games, gamers and the game industry interesting. Gamers are early adaptors. They are quite happy to play with technology, to accept that strong efforts have to be made in order to get minimal advantage, and they are used to the fact that games have to be mastered by training, allowing them to go from one level to the next level and to get a higher ranking than their competitors”. However, we cannot take for granted that the kind of BCI technology (sensors and brain signals) that the game industry would eventually develop will automatically be appropriate for functional interaction. This is the case for current “BCI” game sensors that are limited in number and position over the users head (normally just over the forefront, where there is no hair).\nOne concern with the mass-produced BCI games is proper evaluation; namely, how to prove that the user's brainwaves are the actual control signals driving the game. Of course, from a hybrid BCI perspective, gamers can (and must) also use other physiological signals and interaction modalities. The point, however, is to demonstrate that users have a sufficient degree of mental control for those aspects of the game that require so, as advertised. This issue also raises the question of how to evaluate games as a whole to ensure that they provide a valuable and enjoyable experience. In this respect, the Fun of Gaming (FUGA) project advocates a multi-dimensional evaluation using self-reports, behavioral observations and psychophysiological measures as each in itself is insufficient to get the full picture (IJsselsteijn et al., 2008). Much of the research in pleasure and satisfaction in entertainment focuses on gaming but some might be applied to entertainment in general. For example, “fun” in a game includes challenge, curiosity, fantasy, and Csikszentmihalyi's theory of flow (level of engagement that one is completely absorbed in the current activity and enjoys it in itself without any need for future benefit), but these can also apply to interactive art and creativity (and by extension interactive media, Costello and Edmonds, 2007). Only such a kind of evaluation will prove beneficial for BCI games in general, and for disabled people in particular. Otherwise, BCI games will be just another “fast-food toy” that customers buy and stop using quickly, thus risking to seriously damage the credibility of the BCI field – such a blow that early in its development stage could cripple the field, by projecting a negative image to the public, other industrial sectors, and to funding agencies.\n\nVirtual reality\nBecause BCI are a closed-loop systems, feedback is an important component. Various methods of providing feedback can inform the participant about success or failure of an intended act. Thus, feedback either supports reinforcement during the learning/training process or in controlling the application. In particular, the use of virtual reality (VR) has been proven to be an interesting and promising way to realize such feedback.\nSeveral prototypes have enabled users to navigate in virtual scenes solely by means of their oscillatory cerebral activity, recorded on the scalp via EEG electrodes. Healthy participants were exploring virtual spaces (Leeb et al., 2007b,c; Scherer et al., 2008; Ron-Angevin et al., 2009), were manipulating virtual objects (Lecuyer et al., 2008), and a spinal-cord injured patient was controlling a wheelchair through a virtual street (Leeb et al., 2007a). Additionally, evoked potentials (P300, Bayliss, 2003; and SSVEPs, Lalor et al., 2005) have been used to control VR feedback as well. In these studies, BCI users who use immersive Virtual Environments (VEs) make fewer errors, report that BCIs are easier to learn and use, and state that they enjoy BCI use more (Leeb et al., 2006, 2007b; Ron-Angevin et al., 2009). These benefits may occur because VEs enhance vividness and mental effort, which may lead to more distinct brain patterns and improve pattern recognition performance. Nevertheless, VR technologies provide motivating, safe, and controlled conditions that enable improvement of BCI learning as well as the investigation of the brain responses and neural processes involved, meanwhile testing new virtual prototypes.\n\nMusic browsing\nSince the introduction of mp3 compression technology and easy-to-use mobile music players (such as Apple's iPod player, and iTunes software), there has been an explosion in the use of computers for listening to music. For example, listeners can create “playlists” of their favorite tracks to listen to, burn tracks to CDs, or share with friends. In many cases, though, typical users find that this requires too much effort. Recently a lot of publicity has been given to the “Genius” feature on Apple's widely used iTunes software, although a range of alternatives have been in existence for some time (e.g., websites such as last.fm, pandora.com, www.spotify.com). Moodplayer is an application that lets you create playlists on the go based on your mood and the mood of songs in your music library, and which can be installed on iPhones or Nokia phones. This is a natural application area for BCI. Although no BCI music browser has been developed yet, some BCI for music composition (Miranda, 2006) do exist.\n\nPhoto browsing\nExisting research has focused on determining what kinds of photographs people have, what tasks they perform with them (and what tasks they would like to perform but cannot), and what structure the collections have. In particular, Frohlich et al. (2002) and Kirk et al. (2006) examine how users utilize their personal digital photographs. Both noted the general lack of organization of digital photographs, and the use of very simple exploration techniques. Complex searching activities were not found to be of particular benefit to users when dealing with their personal archives. Rodden and Wood (2003) also examined digital photograph activities, observing a distinct lack of annotation activity and the utility of temporal structuring in exploration of photo archives. An individual picking up an interactive photo display often does not have a clear idea of what images he or she wishes to see. This partially explains why many sophisticated and powerful organization and query interfaces are not widely adopted. Few users know what they want to see before they begin; fewer still are able to distill those intents into meaningful queries across the attributes of images which the system observes. Photo journalists, archivists or other workers with very specific and well-defined needs may benefit from such interactions. This use case, however, is exceedingly rare among home users exploring personal photograph collections.\nAlthough users may not have a definite idea of what images they would be interested in seeing, or are unable to communicate their preferences given the available metadata attributes, they may instead be able to iteratively refine selections to find images of interest. The presentation of a sample from a large set of images can stimulate memories; user can then follow paths through photo space by indicating that they would like to see more images “similar” to one or more of those displayed. Using rich similarity metrics is essential in obtaining effective navigation by this means. This style of interaction has much in common with Bates’ “berry picking” model of information retrieval (Bates, 1989). In this model, users wander through an information space, finding results and modifying their queries as they go. The final goal of the user adapts as they bounce through the results from each previous query. This approach is well-suited to develop BCI tools for photo browsing. The idea is to combine BCI with simple image search techniques. Users will mentally select pictures representing possible categories in their photo archives with a P300-based BCI, and image search techniques will provide similar pictures. In fact, there is some preliminary work that follow this P300 approach (Touyama, 2008). Also of interest is the use of rapid serial visual presentation (RSVP) paradigms for image triage (Gerson et al., 2006). In this approach, users watch many images presented a high rate (say, 4 Hz) and the presence of a P300 evoked potential indicates images of interest that are ranked on top of the final selection."}