  Automated interpretation of signals yields many impressive applications from
the area of affective computing and human activity recognition (HAR). In this
paper we ask the question about possibility of cognitive activity recognition
on the base of particular set of signals. We use recognition of the game played
by the participant as a playground for exploration of the problem. We build
classifier of three different games (Space Invaders, Tetris, Tower Defence) and
inter-game pause. We validate classifier in the player-independent and
player-dependent scenario. We discuss the improvement in the player-dependent
scenario in the context of biometric person recognition. On the base of the
results obtained in game classification, we consider potential applications in
smart surveillance and quantified self.
