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Background  Brain-computer interface methodology based on self-regulation of slow-cortical potentials (SCPs) of the EEG (electroencephalogram) was used to assess conditional associative learning in one severely paralyzed, late-stage ALS patient. After having been taught arbitrary stimulus relations, he was evaluated for formation of equivalence classes among the trained stimuli.    Methods  A monitor presented visual information in two targets. The method of teaching was matching to sample. Three types of stimuli were presented: signs (A), colored disks (B), and geometrical shapes (C). The sample was one type, and the choice was between two stimuli from another type. The patient used his SCP to steer a cursor to one of the targets. A smiley was presented as a reward when he hit the correct target. The patient was taught A-B and B-C (sample – comparison) matching with three stimuli of each type. Tests for stimulus equivalence involved the untaught B-A, C-B, A-C, and C-A relations. An additional test was discrimination between all three stimuli of one equivalence class presented together versus three unrelated stimuli. The patient also had sessions with identity matching using the same stimuli.    Results  The patient showed high accuracy, close to 100%, on identity matching and could therefore discriminate the stimuli and control the cursor correctly. Acquisition of A-B matching took 11 sessions (of 70 trials each) and had to be broken into simpler units before he could learn it. Acquisition of B-C matching took two sessions. The patient passed all equivalence class tests at 90% or higher.    Conclusion  The patient may have had a deficit in acquisition of the first conditional association of signs and colored disks. In contrast, the patient showed clear evidence that A-B and B-C training had resulted in formation of equivalence classes. The brain-computer interface technology combined with the matching to sample method is a useful way to assess various cognitive abilities of severely paralyzed patients, who are without reliable motor control.

Conditional associative learning examined in a paralyzed patient with amyotrophic lateral sclerosis using brain-computer interface technology