In this chapter we present an overview how computational intelligence is used to discover patterns in brain signals. Computational intelligence is the key to identify and extract features also to classify or discover discriminating characteristics in signals. A typical BCI system is comprised of a Signal Processing module which can be further broken down into four submodules namely, Pre-processing, Feature Extraction, Feature Selection and Classification. The objective of EEG based Brain-Computer Interface (BCI) systems is to extract specific signature of the brain activity and to translate them into command signals to control external devices or understand human brains action mechanism to stimuli. Moreover, if the signals are collected by a consumer grade wireless EEG acquisition device, the amount of interference is ever more complex to avoid, and it becomes impossible to see any sorts of pattern without proper use of computational intelligence to discover patterns. Application of appropriate computational intelligence is must to make sense of the raw EEG signals. However, EEG signals are inherently noise-prone, and it is not possible for human to see patterns in raw signals most of the time. If analyzed and patterns are recognized properly this has a high potential application in medicine, psychology, rehabilitation, and many other areas. We recommend mixing video and live lectures and using stimuli evoking and strengthening active engagements.Įlectroencephalography (EEG) captures brain signals from Scalp. These research results can prompt instructors how to construct training materials and implement additional stimuli grabbing student's attention. Reactions to video stimuli in both experimental groups were opposite as for image stimuli. Image stimuli activated different reactions: in a live lecture it slightly tweaked student attentiveness, while in avatar-based lecture attentiveness was lowered. The highest concentration, attentive‐ ness and active engagement was observed during the avatar-based lecture and complex human stimuli, while in live lecture all the stimuli activated approxi‐ mately the same response. Changes of attention to lecture materials were measured using triple indicators: affective regulation monitored during all the lecture period with Muse portable headband device cognitive self-regulation was measured before the lecture using questionnaire technique behavioral regulation was observed using video recording through the entire lecture period. Experi‐ menting group consisted of 10 students, age 20–24, 4 females and 6 males. Avatar was created and animated with CrazyTalk software. Stimuli and study materials for the live and virtual avatar-based lectures were developed following the same experiment lecture model. At the end of each period different stimuli were exposed: human interrupted the lecture instructor presented slides video materials and intensive body movements. Each experiment lasted 30 min and was divided into 5 periods (10-5-5-5-5 min each). This paper presents an empirical study on student's engagement change to live and virtual lectures with complex, picture, video and human body movement stimuli. The cognition levels of the learner are facilitated and motivation provided for better performance.Īffective engagement to university lectures needs external stimula‐ tion. This process helps to assess the learning outcome and academic performance of more than one learner by using BCI in the real life environment. Analysis the EEG signals is done to interpret the brain signals based on frequency ranges depicting different waves Alpha, Beta, Gamma waves, an Algorithm was implemented in classifying the concentration levels as learning outcomes by taking Beta Waves into consideration for a set of students. This paper explains how to analyses BCI signals in the real life classroom environment ,method of study the Signals of an individual while performing a predefined task, procedure to procure, understand and evaluate the EEG signals. If the feedback is not considerate to the standard value, further motivation can be provided to enhance the learning process. The facilitator can design the content in suitable to the learner learning style and provide Neuro-Feedback of the concentration applied and knowledge gained through computer based assessment. BCI in Classroom will assist the facilitator and well as the learner. Brain Computer Interface is a technology where the real time brain activity is recorded and transmitted to an analyzer to interpret.
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