publications([{ "lang": "en", "publisher": "Springer", "doi": "http://doi.org/10.1007/978-3-319-22701-6_37", "title": "Towards Brain Computer Interfaces for Recreational Activities: Piloting a Drone", "abstract": "Active Brain Computer Interfaces (BCIs) allow people to exert voluntary control over a computer system: brain signals are captured and imagined actions (movements, concepts) are recognized after a training phase (from 10 minutes to 2 months). BCIs are confined in labs, with only a few dozen people using them outside regularly (e.g. assistance for impairments). We propose a “Co-learning BCI” (CLBCI) that reduces the amount of training and makes BCIs more suitable for recreational applications. We replicate an existing experiment where the BCI controls a drone and compare CLBCI to their Operant Conditioning (OC) protocol over three durations of practice (1 day, 1 week, 1 month). We find that OC works at 80% after a month practice, but the performance is between 60 and 70% any earlier. In a week of practice, CLBCI reaches a performance of around 75%. We conclude that CLBCI is better suited for recreational use. OC should be reserved for users for whom performance is the main concern.", "authors": { "1": { "first_name": "Nataliya", "last_name": "Kos'myna" }, "2": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "3": { "first_name": "Bertrand", "last_name": "Rivet" } }, "year": 2015, "uri": "http://iihm.imag.fr/publication/KTR15b/", "pages": "506-522", "bibtype": "inproceedings", "id": 726, "abbr": "KTR15b", "address": "Bamberg, Germany", "date": "2015-09-15", "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet", "booktitle": "Proceedings of the 15th IFIP TC13 Conference on Human-Computer Interaction (INTERACT'15)", "type_publi": "icolcomlec" }, { "lang": "en", "type_publi": "icolcomlec", "doi": "https://doi.org/10.1109/EUSIPCO.2015.7362880", "title": "Operationalization of Conceptual Imagery for BCIs", "abstract": "We present a Brain Computer Interface (BCI) system in an asynchronous setting that allows classifying objects in their semantic categories (e.g. a hammer is a tool). For training, we use visual cues that are representative of the concepts (e.g. a hammer image for the concept of hammer). We evaluate the system in an offline synchronous setting and in an online asynchronous setting. We consider two scenarios: the first one, where concepts are in close semantic families (10 subjects) and the second where concepts are from distinctly different categories (10 subjects). We find that both have classification accuracies of 70% and above, although more distant conceptual categories lead to 5% more in classification accuracy.", "authors": { "1": { "first_name": "Nataliya", "last_name": "Kos'myna" }, "2": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "3": { "first_name": "Bertrand", "last_name": "Rivet" } }, "year": 2015, "uri": "http://iihm.imag.fr/publication/KTR15c/", "pages": "2726-2730", "bibtype": "inproceedings", "id": 733, "abbr": "KTR15c", "address": "Nice, France", "date": "2015-08-30", "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet", "booktitle": "European Signal Processing Conference (EUSIPCO'2015)" }, { "chapter": 12, "publisher": "ACM", "doi": "http://doi.acm.org/10.1145/2723162", "lang": "en", "uri": "http://iihm.imag.fr/publication/KTR15a/", "title": "Adding Human Learning in Brain Computer Interfaces (BCIs): Towards a Practical Control Modality", "bibtype": "article", "journal": "ACM Trans. Comput.-Hum. Interact. (TOCHI) 22, 3, Article 12 (May 2015), 37 pages. ", "year": 2015, "number": 3, "pages": "1-37", "volume": 22, "id": 717, "abbr": "KTR15a", "authors": { "1": { "first_name": "Nataliya", "last_name": "Kos'myna" }, "2": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "3": { "first_name": "Bertrand", "last_name": "Rivet" } }, "date": "2015-03-23", "type": "Revues internationales avec comité de lecture", "abstract": "In this article we introduce CLBCI (Co-Learning for Brain Computer Interfaces), a BCI architecture based on co-learning, where users can give explicit feedback to the system rather than just receiving feedback. CLBCI is based on minimum distance classification with Independent Component Analysis (ICA) and allows for shorter training times compared to classical BCIs, as well as a faster learning in users and a good performance progression. We further propose a new scheme for real-time two-dimensional visualization of classification outcomes using Wachspress coordinate interpolation. It allows us to represent classification outcomes for n classes in simple regular polygons. Our objective is to devise a BCI system that constitutes a practical interaction modality that can be deployed rapidly and used on a regular basis. We apply our system to an event-based control task in the form of a simple shooter game where we evaluate the learning effect induced by our architecture compared to a classical approach. We also evaluate how much user feedback and our visualization method contribute to the performance of the system.", "type_publi": "irevcomlec" }, { "chapter": 26, "publisher": "ACM", "doi": "https://doi.org/10.1145/2808228", "lang": "en", "uri": "http://iihm.imag.fr/publication/KTR15e/", "title": "Conceptual Priming for In-game BCI Training", "bibtype": "article", "journal": "ACM Trans. Comput.-Hum. Interact.", "year": 2015, "number": 5, "pages": "1-25", "volume": 22, "id": 737, "abbr": "KTR15e", "authors": { "1": { "first_name": "Nataliya", "last_name": "Kos'myna" }, "2": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "3": { "first_name": "Bertrand", "last_name": "Rivet" } }, "date": "2015-10-01", "type": "Revues internationales avec comité de lecture", "abstract": "Using Brain Computer Interfaces (BCIs) as a control modality for games is popular. However BCIs require prior training before playing, which is hurtful to immersion and player experience in the game. For this reason, we propose an explicit integration of the training protocol in game by a modification of the game environment to enforce the synchronicity with the BCI system and to provide appropriate instructions to user. We then dissimulate the synchronicity in the game mechanics by using priming to mask the training instruction (implicit stimuli). We conduct an evaluation of the effects on game experience compared to standard BCI training on 36 subjects. We use the game experience questionnaire (GEQ) coupled with reliability analysis (Cronbach's alpha). The integration does not change the feeling of competence (3/4). However, flow and immersion increase sizably with explicit training integration (2.78 and 2.67/4 from 1.79/4 and 1.52/4) and even more with the implicit training integration (3.27/4 and 3.12/4).", "type_publi": "irevcomlec" }, { "lang": "en", "publisher": "ACM", "doi": "http://doi.acm.org/10.1145/2782758", "uri": "http://iihm.imag.fr/publication/KTR15d/", "title": "Brains, computers, and drones: think and control!", "bibtype": "misc", "journal": "ACM Interactions", "year": 2015, "number": 4, "pages": "44-47", "volume": 22, "id": 734, "abbr": "KTR15d", "authors": { "1": { "first_name": "Nataliya", "last_name": "Kos'myna" }, "2": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "3": { "first_name": "Bertrand", "last_name": "Rivet" } }, "date": "2015-06-30", "type": "Diffusion de la connaissance, vulgarisation scientifique", "abstract": "Imagine you could control the world with your thoughts. Sounds appealing, doesn’t it? There is a technology that can capture your brain activity and issue commands to computer systems, such as robots, prosthetics, and games. Indeed, brain-computer interfaces (BCIs) have been around since the 1970s, and have improved with each passing decade. You might wonder: “Wait! If this technology has been around all this time, how come we’re not all using it? I mean, we hear about great applications sometimes in the press—controlling a drone, for instance—but then nothing seems to come of it. Why is that?”", "type_publi": "diffusion" }, { "lang": "en", "type_publi": "icolcomlec", "doi": "https://doi.org/10.1145/2559206.2574820", "title": "Bidirectional Feedback in Motor Imagery BCIs: Learn to Control a Drone within 5 Minutes", "abstract": "Brain Computer Interface systems rely on lengthy training phases that can last up to months due to the inherent variability in brainwave activity between users. We propose a BCI architecture based on the co-learning between the user and the system through different feedback strategies. Thus, we achieve an operational BCI within minutes. We apply our system to the piloting of an AR.Drone 2.0 quadricopter. We show that our architecture provides better task performance than traditional BCI paradigms within a shorter time frame. We further demonstrate the enthusiasm of users towards our BCI-based interaction modality and how they find it much more enjoyable than traditional interaction modalities.", "authors": { "1": { "first_name": "Nataliya", "last_name": "Kos'myna" }, "2": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "3": { "first_name": "Bertrand", "last_name": "Rivet" } }, "year": 2014, "uri": "http://iihm.imag.fr/publication/KTR14a/", "pages": "479-482", "bibtype": "inproceedings", "id": 711, "abbr": "KTR14a", "address": "Toronto, Canada", "date": "2014-04-26", "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet", "booktitle": "CHI'14 Extended Abstracts on Human Factors in Computing Systems. 2014" }, { "lang": "en", "type_publi": "colloque", "doi": "https://doi.org/10.1145/2638728.2638785", "title": "Drone, Your Brain, Ring Course: Accept the Challenge and Prevail!", "abstract": "Brain Computer Interface systems (BCIs) rely on lengthy training phases that can last up to months due to the inherent variability in brainwave activity between users. We propose a BCI architecture based on the co- learning between the user and the system through different feedback strategies. Thus, we achieve an operational BCI within minutes. We apply our system to the piloting of an AR.Drone 2.0 quadricopter with a series of hoops delimiting an exciting circuit. We show that our architecture provides better task performance than traditional BCI paradigms within a shorter time frame. We further demonstrate the enthusiasm of users towards our BCI-based interaction modality and how they find it much more enjoyable than traditional interaction modalities.", "authors": { "1": { "first_name": "Nataliya", "last_name": "Kos'myna" }, "2": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "3": { "first_name": "Bertrand", "last_name": "Rivet" } }, "year": 2014, "uri": "http://iihm.imag.fr/publication/KTR14b/", "pages": "243-246", "bibtype": "inproceedings", "id": 712, "abbr": "KTR14b", "address": "Seattle, USA", "date": "2014-09-13", "type": "Autres conférences et colloques avec actes", "booktitle": "UBICOMP'14 ADJUNCT. 2014" }]);