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@@ -889,6 +889,10 @@ class ReachGraspIO(BlackrockIO):
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if not any(neural_chids):
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asig.annotate(neural_signal=False)
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+ asig.name = "Behavioural Time Series"
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+ asig.descriptions = "This Analogsignal object contains the continuous behavioural time series recorded in " \
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+ "the experiment, including object displacements and measurements of the " \
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+ "gripforce sensors."
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elif all(neural_chids):
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asig.annotate(neural_signal=True)
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@@ -929,6 +933,16 @@ class ReachGraspIO(BlackrockIO):
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filter_type=filter_type
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))
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+ if asig.sampling_rate == pq.Quantity(30000 * pq.Hz):
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+ asig.name = "Raw Neural Time Series"
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+ asig.description = "This Analogsignal object contains the continuous raw neuronal recordings " \
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+ "sampled at high resolution."
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+ if asig.sampling_rate == pq.Quantity(1000 * pq.Hz):
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+ asig.name = "Downsampled Neural Time Series"
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+ asig.description = "This Analogsignal object contains the downsampled continuous neuronal " \
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+ "recordings, where the downsampling was performed on-line by the recording " \
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+ "system."
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+
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self.__annotate_electrode_rejections(asig)
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def __annotate_electrode_rejections(self, obj):
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