Introduction ML in BCI ML in brain research Ethics Mining the mind: Machine learning in brain research Matthias Treder 2016-12-16
Introduction ML in BCI ML in brain research Ethics
Introduction ML in BCI ML in brain research Ethics M EASURING BRAIN ACTIVITY image taken from: Astrand E, Wardak C and Ben Hamed S (2014). “Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications” Front. Syst. Neurosci. 8:144
Introduction ML in BCI ML in brain research Ethics BCI = MACHINE LEARNING + REAL - TIME NEUROIMAGING
Introduction ML in BCI ML in brain research Ethics BCI = MACHINE LEARNING + REAL - TIME NEUROIMAGING
Introduction ML in BCI ML in brain research Ethics BCI = MACHINE LEARNING + REAL - TIME NEUROIMAGING
Introduction ML in BCI ML in brain research Ethics BCI = MACHINE LEARNING + REAL - TIME NEUROIMAGING
Introduction ML in BCI ML in brain research Ethics C ORRECTING ERRORS IN MENTAL TYPEWRITERS Schmidt, Blankertz, Treder (2012), BMC Neuroscience
Introduction ML in BCI ML in brain research Ethics C ORRECTING ERRORS IN MENTAL TYPEWRITERS Schmidt, Blankertz, Treder (2012), BMC Neuroscience Stimulation C A F e m T i
Introduction ML in BCI ML in brain research Ethics C ORRECTING ERRORS IN MENTAL TYPEWRITERS Schmidt, Blankertz, Treder (2012), BMC Neuroscience Classi fi cation Stimulation output correct C A C F error e m T i
fi fi Introduction ML in BCI ML in brain research Ethics C ORRECTING ERRORS IN MENTAL TYPEWRITERS 20 error correct [ µ V] 10 0 − 100 0 100 200 300 400 500 600 700 800 900 1000
Introduction ML in BCI ML in brain research Ethics C ORRECTING ERRORS IN MENTAL TYPEWRITERS 20 error correct [ µ V] 10 0 − 100 0 100 200 300 400 500 600 700 800 900 1000 Accuracy ROC classi fi er A ROC classi fi er B 1 1 1 0.9 0.9 0.9 0.8 0.8 0.8 0.7 0.7 0.7 0.6 Hit rate 0.6 Hit rate 0.6 AUC gbo / auc: 0.83 gbo / auc: 0.97 bad / auc: 0.85 bad / auc: 0.97 0.5 0.5 0.5 iae / auc: 0.75 iae / auc: 0.96 gbq / auc: 0.62 gbq / auc: 0.96 0.4 0.4 0.4 gbt / auc: 0.8 gbt / auc: 0.93 0.3 0.3 0.3 iac / auc: 0.83 iac / auc: 0.91 gbn / auc: 0.82 gbn / auc: 0.87 0.2 0.2 0.2 gbw / auc: 0.87 gbw / auc: 0.84 iau / auc: 0.69 iau / auc: 0.79 0.1 0.1 0.1 ClassifierA mk / auc: 0.69 mk / auc: 0.75 ClassifierB fat / auc: 0.95 fat / auc: 0.99 0 0 0 Mean 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Participant False alarm rate False alarm rate
Introduction ML in BCI ML in brain research Ethics ML IN MEMORY RESEARCH
Introduction ML in BCI ML in brain research Ethics ML IN MEMORY RESEARCH Pre- 22 92 13 34 encoding 83 37 40 49 6 77 2 49 12 Encoding later remembered later remembered later forgotten later forgotten Post- 10 72 encoding 31 71 84 18 10 33 98 70 7 89 23 ? ? Retrieval remembered forgotten remembered forgotten
Introduction ML in BCI ML in brain research Ethics ML IN MEMORY RESEARCH
Introduction ML in BCI ML in brain research Ethics N EUROETHICS ◮ Cellular, molecular, cognitive neuroscience
Introduction ML in BCI ML in brain research Ethics N EUROETHICS ◮ Cellular, molecular, cognitive neuroscience ◮ Incidental findings in healthy participants
Introduction ML in BCI ML in brain research Ethics N EUROETHICS ◮ Cellular, molecular, cognitive neuroscience ◮ Incidental findings in healthy participants ◮ Altering brain function (eg deep brain stimulation)
Introduction ML in BCI ML in brain research Ethics N EUROETHICS ◮ Cellular, molecular, cognitive neuroscience ◮ Incidental findings in healthy participants ◮ Altering brain function (eg deep brain stimulation) ◮ Brain enhancement
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: E XPECTATIONS ◮ Traumatic brain injury: impaired capacity to judge costs/benefits
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: E XPECTATIONS ◮ Traumatic brain injury: impaired capacity to judge costs/benefits ◮ Motor skills automatic, BCI operation requires sustained attention and cognitive effort
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: E XPECTATIONS ◮ Traumatic brain injury: impaired capacity to judge costs/benefits ◮ Motor skills automatic, BCI operation requires sustained attention and cognitive effort ◮ High expectations due to media bias
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: E XPECTATIONS ◮ Traumatic brain injury: impaired capacity to judge costs/benefits ◮ Motor skills automatic, BCI operation requires sustained attention and cognitive effort ◮ High expectations due to media bias ◮ Psychological harm: distress, loss of behavioral control
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: E XPECTATIONS ◮ Traumatic brain injury: impaired capacity to judge costs/benefits ◮ Motor skills automatic, BCI operation requires sustained attention and cognitive effort ◮ High expectations due to media bias ◮ Psychological harm: distress, loss of behavioral control ◮ Selection: Equal opportunity for all vs failed expectations
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: R ESEARCH VS CLINICAL PRACTICE
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: R ESEARCH VS CLINICAL PRACTICE
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: R ESEARCH VS CLINICAL PRACTICE Improvement: proof-of-concept and parameter tuning with volunteers, transfer to patients image taken from: McCullagh P, Lightbody G, Zygierewicz J, Kernohan W. (2014). “Ethical challenges associated with the development and deployment of brain computer interface technology.” Neuroethics 7:109–122.
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: B ENEFITS AND RISKS Trade-off: Levels of invasiveness ◮ EEG (noninvasive): e.g. motor cortex signals smeared
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: B ENEFITS AND RISKS Trade-off: Levels of invasiveness ◮ EEG (noninvasive): e.g. motor cortex signals smeared ◮ ECog (epidural implant): risk of infection and hemorrhage
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: B ENEFITS AND RISKS Trade-off: Levels of invasiveness ◮ EEG (noninvasive): e.g. motor cortex signals smeared ◮ ECog (epidural implant): risk of infection and hemorrhage ◮ Microelectrode array (subdural implant): risk of tissue death
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: C OMMUNICATION ◮ Patient groups
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: C OMMUNICATION ◮ Patient groups ◮ Minimally conscious: residual awareness
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: C OMMUNICATION ◮ Patient groups ◮ Minimally conscious: residual awareness ◮ Locked in: conscious but lack capacity for voluntary body motion
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: C OMMUNICATION ◮ Patient groups ◮ Minimally conscious: residual awareness ◮ Locked in: conscious but lack capacity for voluntary body motion ◮ Informed consent
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: C OMMUNICATION ◮ Patient groups ◮ Minimally conscious: residual awareness ◮ Locked in: conscious but lack capacity for voluntary body motion ◮ Informed consent ◮ ‘Talking’ via ML algorithm, rather than to the patient directly
Introduction ML in BCI ML in brain research Ethics E THICS IN BCI: C OMMUNICATION ◮ Patient groups ◮ Minimally conscious: residual awareness ◮ Locked in: conscious but lack capacity for voluntary body motion ◮ Informed consent ◮ ‘Talking’ via ML algorithm, rather than to the patient directly ◮ Caregivers might overestimate system (yes-no responses)
Introduction ML in BCI ML in brain research Ethics M INING THE BRAIN USING ML
Introduction ML in BCI ML in brain research Ethics M INING THE BRAIN USING ML Privacy of thoughts ◮ unconscious intentions [Zander et al., Dec 2016, PNAS]
Introduction ML in BCI ML in brain research Ethics M INING THE BRAIN USING ML Privacy of thoughts ◮ unconscious intentions [Zander et al., Dec 2016, PNAS] ◮ psychological traits
Introduction ML in BCI ML in brain research Ethics M INING THE BRAIN USING ML Privacy of thoughts ◮ unconscious intentions [Zander et al., Dec 2016, PNAS] ◮ psychological traits ◮ neuromarketing
Introduction ML in BCI ML in brain research Ethics M INING THE BRAIN USING ML Privacy of thoughts ◮ unconscious intentions [Zander et al., Dec 2016, PNAS] ◮ psychological traits ◮ neuromarketing ◮ brain fingerprinting
Introduction ML in BCI ML in brain research Ethics M INING THE BRAIN USING ML Privacy of thoughts ◮ unconscious intentions [Zander et al., Dec 2016, PNAS] ◮ psychological traits ◮ neuromarketing ◮ brain fingerprinting ◮ ‘thought police’?
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