{"product_id":"thinkgear-sdk-mental-effort-and-familiarity-beta-preview","title":"Mental Effort and Familiarity Beta Preview","description":"\u003cblockquote\u003e\r\n\u003ch1\u003e\u003cstrong\u003eAndroid Version NOW AVAILABLE. Select your Platform from the pulldown menu. \u003c\/strong\u003e\u003c\/h1\u003e\r\n\u003c\/blockquote\u003e\r\n\u003ch3\u003e\u003c\/h3\u003e\r\n\u003cp\u003e \u003c\/p\u003e\r\n\u003cp\u003e \u003c\/p\u003e\r\n\u003cp\u003eTry the new preview of the ThinkGear SDK for .NET - Mental Effort and Familiarity Beta. \u003c\/p\u003e\r\n\u003cp\u003e \u003c\/p\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eWhat do Mental Effort and Familiarity tell about my mind?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003eMental Effort measures the mental workload while performing a task. The harder you (your brain) work on a task, the higher the value. It could tell you many things such as – is your brain having a difficult time with the task?\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eFamiliarity measures the process of learning while performing a task. For some cases, it reflects how well you are doing with the task. By observing trends, you can better understand your own learning process.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eBoth of them could be applied on tasks that are physical (e.g. drawing) or mental (e.g. recitation) in nature.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eI would like to try out how they work first.\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003eOur Demo Apps are located in the Demo folder of this SDK. Explore the executable demo applications and then try integrating the .NET code into your project.\u003c\/div\u003e\r\n\u003cbr\u003e \u003cbr\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eCan you provide more example tasks that we can try on with Mental Effort and \u003c\/strong\u003e\u003cstrong\u003eFamiliarity?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003eWe have compiled some of the tasks below:\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eMemorization: Poker card memorization, Random number memorization\u003c\/div\u003e\r\n\u003cdiv\u003eCalculations: “Addition Aliens Attack”, “Math 24”\u003c\/div\u003e\r\n\u003cdiv\u003eMovements: fighting games, American Sign Language practicing, Mirror tracing, coloring pages, We have also conducted many experiments on those tasks – and Mental Effort and Familiarity tells a lot! \u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003e(See \\docs\\Algorithm Docs\\Mental Effort and Familiarity\\Experiment Examples and Test Results\\).\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eDo note that the algorithms might or might not be applicable to any tasks you come up with outside our example tasks.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eWhat is the key to reading the data? Are there any ways to interpret them?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003ePlease read the document “Reading Mental Effort and Familiarity.pdf” in the current folder. Additionally, your understanding of the algorithm can be reinforced by your own experience using the algorithms (e.g. while trying out our demo apps).\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003e\u003cspan style=\"font-size: 14px; line-height: 1.5;\"\u003e\u003c\/span\u003e\u003c\/div\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 14px; line-height: 1.5;\"\u003eI know what to expect in the data now, but why are the values in \u003c\/span\u003earbitrary units?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003eThe values are in arbitrary unit. You can see the fluctuation with real numbers as well. For example, for a series of Mental Effort Indexes collected while doing a task (see the graph below – screenshot from our Mental Effort Demo “Pop Quiz”), even without a Y-axis, you see the fluctuation of effort over time.\u003c\/div\u003e\r\n\u003cbr\u003e \u003cbr\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eWhat is it about “Idle Profiles” and\/or “common baselines” that are mentioned \u003c\/strong\u003e\u003cstrong\u003ein the materials?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003eEvery Idle Profile contains some EEG data corresponding to a person that have been tested by the Mental Effort and\/or Familiarity algorithm(s). The EEG data stored records the relaxed state (eyes open, doing nothing) of the person’s brain. The Idle Profile serves as a reference point for Mental Effort and Familiarity normalization. \u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eOur implementation of the Idle Profile features in the SDK allows you to re-use the profile again and again – thus relieving the same person of repeated sessions of 1-minute idleness.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eThe Idle Profile also provides a clean start for both algorithms, and thus has to been plugged in to the start of a session whenever you are needing a fresh start in your app (e.g. Your app refreshing the task without closing and re-opening).\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eIf you are digging into our materials, you may come across the term “common baseline”. The “common baseline” basically equates to the “Idle Profile”, where “common baseline” is a more scientific term.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eWhat is the concept behind the Familiarity?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003e\u003c\/div\u003e\r\n\u003cdiv\u003eFamiliarity means learning (depending hugely on the task). When we define learning, we have to define the ability that is being learnt. For example, when a kid works on his penmanship homework, the ability is his\/her motor ability. When someone is memorizing the set of road signs, the ability is his\/her \u003cspan style=\"font-size: 14px; line-height: 1.5;\"\u003ememorization of signs.\u003c\/span\u003e\n\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eAnother point is that learning can be stunned – we are constantly challenged by various aspects of a task, and each challenge affects our Familiarity values.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eThat brings us to: the Interpretation of Familiarity indexes is not as intuitive as Mental Effort indexes. Whereas Mental Effort can be read as a straightforward measurement of a subject's mental load at any given time, Familiarity must always take the task and the user's engagement with the task into account as described above.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eAre there research papers to support your algorithms?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003eThey’re at \\docs\\Algorithm Docs\\Mental Effort and Familiarity\\Research Papers\\. Both papers involve studies that measures frontal EEG activities by a single-channel mobile EEG system. The paper titled  “Evaluation of Mental Workload in Visual-Motor Task: Spectral Analysis of Single-Channel Frontal EEG” showed correlation between overall perceived difficulty of a visual-motor task and significant modulation on the frontal EEG spectra. Another paper titled “During Motor Skill Acquisition: Task Familiarity Monitoring Using Single-Channel EEG” showed correlation between overall familiarity level of the task and the frontal EEG activities in delta band of the whole trial and gamma band at the beginning of each trial.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eIn addition, we have various experiments done (see \\docs\\Algorithm Docs\\Mental Effort and Familiarity\\Experiment Examples and Test Results\\). The document titled “Applying Mental Effort and Familiarity Algorithms in Maze Games” involves various Mental Effort and Familiarity results measured when the subject explore Mazes of various difficulties. Another document titled “Subjective Testing of Mental Effort and Familiarity Algorithms with Video Games” documented the tests on the usage model of Mental Effort and Familiarity algorithms on different video games such as “Coloring Pages” and “Street Fighter”. The experiments conducted confirm the prowess of the algorithms and help us understand more about the behavior of the data.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/div\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eI’m a developer. Where do I start?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eYou should refer to the development guide (ThinkGear SDK for .NET - Development Guide and API Reference.pdf). API references related to these algorithms are under the sections API Reference\/Connector class and API Reference\/Algorithms.MentalEffort class. Technical descriptions and a step-by-\u003c\/div\u003e\r\n\u003cdiv\u003estep implementation guide can be found in the sections ThinkGear Data Types\/EEG\/MENTAL EFFORT and ThinkGear Data Types\/EEG\/FAMILIARITY.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eWe also have a sample C# project demonstrating a basic implementation of Mental Effort and Familiarity (with data collection and basic analysis). It could be found in \\Sample Projects and Demos\\HelloMEandF\\.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003eAdditional platforms are forthcoming.\u003c\/div\u003e\r\n\u003cbr\u003e \u003cbr\u003e\r\n\u003cdiv\u003e\u003cem\u003e\u003cstrong\u003eI want to know more. Who should I contact?\u003c\/strong\u003e\u003c\/em\u003e\u003c\/div\u003e\r\n\u003cdiv\u003eYou can reach us at support@neurosky.com.\u003c\/div\u003e\r\n\u003cbr\u003e\r\n\u003cdiv\u003e\u003cspan style=\"font-size: 14px; line-height: 1.5;\"\u003e\u003c\/span\u003e\u003c\/div\u003e\r\n\u003cdiv\u003e\n\u003cspan style=\"font-size: 14px; line-height: 1.5;\"\u003eFor your reference, you may check out our Developer Site at: \u003c\/span\u003e\u003ca href=\"http:\/\/developer.neurosky.com\/\" target=\"_blank\" style=\"font-size: 14px; line-height: 1.5;\"\u003ehttp:\/\/developer.neurosky.\u003cwbr\u003e\u003c\/wbr\u003ecom\/\u003c\/a\u003e\u003cb\u003e\u003c\/b\u003e\n\u003c\/div\u003e","brand":"NeuroSky","offers":[{"title":"Android Mental Effort and Familiarity","offer_id":1130659533,"sku":"DDL-MEF001-AND","price":0.0,"currency_code":"CAD","in_stock":true},{"title":".NET Mental Effort and Familiarity","offer_id":1130659537,"sku":"DDL-MEF001-PC","price":0.0,"currency_code":"CAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0699\/8419\/products\/preview2.png?v=1426681603","url":"https:\/\/neurosky-dev.myshopify.com\/products\/thinkgear-sdk-mental-effort-and-familiarity-beta-preview","provider":"Neurosky Dev","version":"1.0","type":"link"}